Machine control using a predictive speed map

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvester can also be fittedwith different types of heads to harvest different types of crops.

A variety of different conditions in fields have a number of deleteriouseffects on the harvesting operation. Therefore, an operator may attemptto modify control of the harvester, upon encountering such conditionsduring the harvesting operation.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to examples that solveany or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of a combine harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving a prior information map, detecting aspeed characteristic, and generating a functional predictive speed mapfor use in controlling the agricultural harvester during a harvestingoperation.

FIG. 6 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 7 shows a flow diagram illustrating one example of the operation ofan agricultural harvester in receiving a speed map and detecting anin-situ sensor input in generating a functional predictive sensor datamap.

FIG. 8 is a block diagram showing some examples of in-situ sensors.

FIG. 9 is a block diagram showing one example of a control zonegenerator.

FIG. 10 is a flow diagram illustrating one example of the operation ofthe control zone generator shown in FIG. 9.

FIG. 11 illustrates a flow diagram showing an example of the operationof a control system in selecting a target settings value to control theagricultural harvester.

FIG. 12 is a block diagram showing one example of an operator interfacecontroller.

FIG. 13 is a flow diagram illustrating one example of an operatorinterface controller.

FIG. 14 is a pictorial illustration showing one example of an operatorinterface display.

FIG. 15 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 16-18 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 19 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester and inprevious figures.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone example may be combined with the features, components, and/or stepsdescribed with respect to other examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with predicted or priordata, to generate a predictive map and, more particularly, a predictivespeed map. In some examples, the predictive speed map can be used tocontrol an agricultural work machine, such as an agricultural harvester.As discussed above, it may improve the performance of the agriculturalharvester to control the speed of the agricultural harvester when theagricultural harvester engages different conditions in the field. Forinstance, if the crops have reached maturity, the weeds may still begreen, thus increasing the moisture content of the biomass that isencountered by the agricultural harvester. This problem may beexacerbated when the weed patches are wet (such as shortly after arainfall or when weed patches contain dew) and before the weeds have hada chance to dry. Thus, when the agricultural harvester encounters anarea of increased biomass, the operator may slow the speed of theagricultural harvester to maintain a constant feed rate of materialthrough the agricultural harvester. Maintaining a constant feed rate maymaintain the performance of the agricultural harvester. Performance ofan agricultural harvester may be deleteriously affected based on anumber of different criteria. Such different criteria may includechanges in biomass, crop state, topography, soil properties, and seedingcharacteristics, or other conditions. Therefore, it may also be usefulto control the speed of the agricultural harvester based on otherconditions that may be present in the field. For example, theperformance of the agricultural harvester may be maintained at anacceptable level by controlling the speed of the agricultural harvesterbased on the biomass encountered by the agricultural harvester, the cropstate of the crop being harvested, the topography of the field beingharvested, soil properties of soil in the field being harvested, seedingcharacteristics in the field being harvested, yield in the field beingharvested, or other conditions that are present in the field.

Also, given a particular speed of the agricultural harvester, it may bedesirable to control controllable subsystems on the agriculturalharvester, in a particular way. For example, if the agriculturalharvester is traveling at a first speed, then it may be desirable tohave the header at a first height whereas if the agricultural harvesteris operating at a second speed it may be desirable to have the header ata second height to maintain feed rate of material through theagricultural harvester at a desirable feed rate.

Some current systems provide vegetative index maps. A vegetative indexmap illustratively maps vegetative index values (which may be indicativeof vegetative growth) across different geographic locations in a fieldof interest. One example of a vegetative index includes a normalizeddifference vegetation index (NDVI). There are many other vegetativeindices that are within the scope of the present disclosure. In someexamples, a vegetative index may be derived from sensor readings of oneor more bands of electromagnetic radiation reflected by the plants.Without limitations, these bands may be in the microwave, infrared,visible or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can be used to identify the presence and locationof vegetation. In some examples, these maps enable vegetation to beidentified and georeferenced in the presence of bare soil, crop residue,or other plants, including crop or other weeds.

In some examples, a biomass map is provided. A biomass mapillustratively maps a measure of biomass in the field being harvested atdifferent locations in the field. A biomass map may be generated fromvegetative index values, from historically measured or estimated biomasslevels, from images or other sensor readings taken during a previousoperation in the field, or in other ways. In some examples, biomass maybe adjusted by a factor representing a portion of total biomass passingthrough the agricultural harvester. For corn, this factor is typicallyaround 50%. For moisture in harvested crop material, this factor istypically 10%-30%. In some examples, the factor may represent a portionof weed material or weed seeds.

In some examples, a crop state map is provided. Crop state may definewhether the crop is down, standing, partially down, the orientation ofdown or partially down crop, and other things. A crop state mapillustratively maps the crop state in the field being harvested atdifferent locations in the field. A crop state map may be generated fromaerial or other images of the field, from images or other sensorreadings taken during a prior operation in the field or in other waysprior to harvesting.

In some examples, a seeding map is provided. A seeding map may mapseeding characteristics such as seed locations, seed variety, or seedpopulation to different locations in the field. The seeding map may begenerated during a past seed planting operation in the field. Theseeding map may be derived from control signals used by a seeder whenplanting seeds or from sensors on the seeder that confirm that a seedwas planted. Seeders may also include geographic position sensors thatgeolocate the seed characteristics on the field.

In some examples, a soil property map is provided. A soil property mapillustratively maps a measure of one or more soil properties such assoil type or soil moisture in the field being harvested at differentlocations in the field. A soil properties map may be generated fromvegetative index values, from historically measured or estimated soilproperties, from images or other sensor readings taken during a previousoperation in the field, or in other ways.

In some examples, other prior information maps are provided. Such priorinformation maps can include a topographic map of the field beingharvested, a predictive yield map for the field being harvested, orother prior information maps.

The present discussion thus proceeds with respect to systems thatreceive a prior information map of a field or map generated during aprior operation and also use an in-situ sensor to detect a variableindicative of one or more of a machine speed and an output from a feedrate control system. The systems generate a model that models arelationship between the prior information values on the priorinformation map and the output values from the in-situ sensor. The modelis used to generate a functional predictive speed map that predicts, forexample, a target machine speed at different locations in the field. Thefunctional predictive speed map, generated during the harvestingoperation, can be presented to an operator or other user or used inautomatically controlling an agricultural harvester during theharvesting operation, or both.

The present discussion also proceeds with respect to systems thatreceive a speed map that maps predicted machine speed values todifferent geographic locations in the field and also use an in-situsensor to detect a variable. The systems generate a model that models arelationship between values on the speed map and the values output bythe in-situ sensor. The model is used to generate a functionalpredictive data map that predicts values at different locations in thefield. The functional predictive data map, generated during theharvesting operation, can be presented to an operator and used inautomatically controlling an agricultural harvester during theharvesting operation.

FIG. 1 is a partial pictorial, partial schematic, illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1, agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms, for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1, agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem 125 also includes discharge beater126, tailings elevator 128, clean grain elevator 130, as well asunloading auger 134 and spout 136. The clean grain elevator moves cleangrain into clean grain tank 132. Agricultural harvester 100 alsoincludes a residue subsystem 138 that can include chopper 140 andspreader 142. Agricultural harvester 100 also includes a propulsionsubsystem that includes an engine that drives ground engaging components144, such as wheels or tracks. In some examples, a combine harvesterwithin the scope of the present disclosure may have more than one of anyof the subsystems mentioned above. In some examples, agriculturalharvester 100 may have left and right cleaning subsystems, separators,etc., which are not shown in FIG. 1.

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Theoperator of agricultural harvester 100 may determine one or more of aheight setting, a tilt angle setting, or a roll angle setting for header102. For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a selected sensitivity level. If the sensitivitylevel is set at a greater level of sensitivity, the control systemresponds to smaller header position errors, and attempts to reduce thedetected errors more quickly than when the sensitivity is at a lowerlevel of sensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by rotor 112 rotating the crop against concaves114. The threshed crop material is moved by a separator rotor inseparator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100includes machine speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward looking image capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors 152 provided in the cleaning subsystem 118.

Machine speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Machine speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axel, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, or a wide variety of other systems or sensorsthat provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118. Separator loss sensor 148 provides a signalindicative of grain loss in the left and right separators, notseparately shown in FIG. 1. The separator loss sensors 148 may beassociated with the left and right separators and may provide separategrain loss signals or a combined or aggregate signal. In some instances,sensing grain loss in the separators may also be performed using a widevariety of different types of sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of fan 120; a concave clearance sensor that sensesclearance between the rotor 112 and concaves 114; a threshing rotorspeed sensor that senses a rotor speed of rotor 112; a chaffer clearancesensor that senses the size of openings in chaffer 122; a sieveclearance sensor that senses the size of openings in sieve 124; amaterial other than grain (MOG) moisture sensor that senses a moisturelevel of the MOG passing through agricultural harvester 100; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, and other crop properties.Crop property sensors may also be configured to sense characteristics ofthe severed crop material as the crop material is being processed byagricultural harvester 100. For example, in some instances, the cropproperty sensors may sense grain quality such as broken grain, MOGlevels; grain constituents such as starches and protein; and grain feedrate as the grain travels through the feeder house 106, clean grainelevator 130, or elsewhere in the agricultural harvester 100. The cropproperty sensors may also sense the feed rate of biomass through feederhouse 106, through the separator 116 or elsewhere in agriculturalharvester 100. The crop property sensors may also sense the feed rate asa mass flow rate of grain through elevator 130 or through other portionsof the agricultural harvester 100 or provide other output signalsindicative of other sensed variables.

Prior to describing how agricultural harvester 100 generates afunctional predictive speed map, and uses the functional predictivespeed map for control, a brief description of some of the items onagricultural harvester 100, and their operation, will first bedescribed. The description of FIGS. 2 and 3 describe receiving a generaltype of prior information map and combining information from the priorinformation map with a georeferenced sensor signal generated by anin-situ sensor, where the sensor signal is indicative of acharacteristic such as one or more characteristics of the field itself,one or more crop characteristics of harvested material, such as crop orgrain present in the field, one or more environmental characteristics ofthe environment of the agricultural harvester or one or morecharacteristics of the agricultural harvester. Characteristics of thefield may include, but are not limited to, characteristics of a fieldsuch as slope, weed intensity, weed type, soil moisture, surfacequality. Crop characteristics can include one or more crop propertiessuch as crop height, crop moisture, crop density, crop state; as well ascharacteristics of grain properties such as grain moisture, grain size,and grain test weight. Environmental characteristics can include weathercharacteristics, and the presence of standing water. Characteristics ofthe agricultural harvester can include characteristics indicative ofmachine settings or operator inputs or characteristics of machineoperation such as machine speed, outputs from different controllers,machine performance such as loss levels, job quality, fuel consumption,and power utilization. A relationship between the characteristic valuesobtained from in-situ sensor signals or values derived therefrom and thespeed map values is identified, and that relationship is used togenerate a new functional predictive map. A functional predictive mappredicts values at different geographic locations in a field, and one ormore of those values may be used for controlling a machine, such as oneor more subsystems of an agricultural harvester. In some instances, afunctional predictive map can be presented to a user, such as anoperator of an agricultural work machine, which may be an agriculturalharvester. A functional predictive map may be presented to a uservisually, such as via a display, haptically, or audibly. The user mayinteract with the functional predictive map to perform editingoperations and other user interface operations. In some instances, afunctional predictive map can be used for one or more of controlling anagricultural work machine, such as an agricultural harvester,presentation to an operator or other user, and presentation to anoperator or user for interaction by the operator or user.

After the general approach is described with respect to FIGS. 2 and 3, amore specific approach for generating a functional predictive speed mapthat can be presented to an operator or user, or used to controlagricultural harvester 100, or both is described with respect to FIGS. 4and 5. Then, using a speed map to control harvesting agriculturalharvester 100 is described. Again, while the present discussion proceedswith respect to the agricultural harvester and, particularly, a combineharvester, the scope of the present disclosure encompasses other typesof agricultural harvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more characteristicsof a field concurrent with a harvesting operation. The in-situ sensors208 generate values corresponding to the sensed characteristics. Theagricultural harvester 100 also includes a predictive model orrelationship generator (collectively referred to hereinafter as“predictive model generator 210”), predictive map generator 212, controlzone generator 213, control system 214, one or more controllablesubsystems 216, and an operator interface mechanism 218. Theagricultural harvester 100 can also include a wide variety of otheragricultural harvester functionality 220. The in-situ sensors 208include, for example, on-board sensors 222, remote sensors 224, andother sensors 226 that sense characteristics of a field during thecourse of an agricultural operation. Predictive model generator 210illustratively includes an information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and system 214 can include otheritems 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and subsystems 216 caninclude a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receive priorinformation map 258. As described below, the prior information map 258includes, for example, a vegetative index map, a biomass map, a cropstate map, a topographic map, a soil property map, a seeding map, or amap from a prior operation. However, prior information map 258 may alsoencompass other types of data that were obtained prior to a harvestingoperation or a map from a prior operation. FIG. 2 also shows that anoperator 260 may operate the agricultural harvester 100. The operator260 interacts with operator interface mechanisms 218. In some examples,operator interface mechanisms 218 may include joysticks, levers, asteering wheel, linkages, pedals, buttons, dials, keypads, useractuatable elements (such as icons, buttons, etc.) on a user interfacedisplay device, a microphone and speaker (where speech recognition andspeech synthesis are provided), among a wide variety of other types ofcontrol devices. Where a touch sensitive display system is provided,operator 260 may interact with operator interface mechanisms 218 usingtouch gestures. These examples described above are provided asillustrative examples and are not intended to limit the scope of thepresent disclosure. Consequently, other types of operator interfacemechanisms 218 may be used and are within the scope of the presentdisclosure.

Prior information map 258 may be downloaded onto agricultural harvester100 and stored in data store 202, using communication system 206 or inother ways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1. In-situ sensors 208 include on-board sensors 222 thatare mounted on-board agricultural harvester 100. Such sensors mayinclude, for instance, any of the sensors discussed above with respectto FIG. 1, a perception sensor (e.g., a forward looking mono or stereocamera system and image processing system), image sensors that areinternal to agricultural harvester 100 (such as the clean grain cameraor cameras mounted to identify material that is exiting agriculturalharvester 100 through the residue subsystem or from the cleaningsubsystem). The in-situ sensors 208 also include remote in-situ sensors224 that capture in-situ information. In-situ data include data takenfrom a sensor on-board the harvester or taken by any sensor where thedata are detected during the harvesting operation. More examples ofin-situ sensors 208 are described below with respect to FIG. 8.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and ametric mapped to the field by the prior information map 258. Forexample, if the prior information map 258 maps a vegetative index valueto different locations in the field, and the in-situ sensor 208 issensing a value indicative of machine speed, then prior informationvariable-to-in-situ variable model generator 228 generates a predictivespeed model that models the relationship between the vegetative indexvalue and the machine speed value. The predictive speed model can alsobe generated based on vegetative index values from the prior informationmap 258 and multiple in-situ data values generated by in-situ sensors208. Then, predictive map generator 212 uses the predictive speed modelgenerated by predictive model generator 210 to generate a functionalpredictive speed map that predicts the a target machine speed sensed bythe in-situ sensors 208 at different locations in the field based uponthe prior information map 258.

In some examples, the type of values in the functional predictive map263 may be the same as the in-situ data type sensed by the in-situsensors 208. In some instances, the type of values in the functionalpredictive map 263 may have different units from the data sensed by thein-situ sensors 208. In some examples, the type of values in thefunctional predictive map 263 may be different from the data type sensedby the in-situ sensors 208 but have a relationship to the type of datatype sensed by the in-situ sensors 208. For example, in some examples,the data type sensed by the in-situ sensors 208 may be indicative of thetype of values in the functional predictive map 263. In some examples,the type of data in the functional predictive map 263 may be differentthan the data type in the prior information map 258. In some instances,the type of data in the functional predictive map 263 may have differentunits from the data in the prior information map 258. In some examples,the type of data in the functional predictive map 263 may be differentfrom the data type in the prior information map 258 but has arelationship to the data type in the prior information map 258. Forexample, in some examples, the data type in the prior information map258 may be indicative of the type of data in the functional predictivemap 263. In some examples, the type of data in the functional predictivemap 263 is different than one of, or both of the in-situ data typesensed by the in-situ sensors 208 and the data type in the priorinformation map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of, or both of, of thein-situ data type sensed by the in-situ sensors 208 and the data type inprior information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 208 or the data type in the priorinformation map 258, and different than the other.

Continuing with the preceding example, in which prior information map258 is a vegetative index map and in-situ sensor 208 senses a valueindicative of machine speed predictive map generator 212 can use thevegetative index values in prior information map 258, and the modelgenerated by predictive model generator 210, to generate a functionalpredictive map 263 that predicts a target machine speed at differentlocations in the field. Predictive map generator 212 thus outputspredictive map 264.

As shown in FIG. 2, predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 208), or a characteristicrelated to the sensed characteristic, at various locations across thefield based upon an information value in prior information map 258 atthose locations and using the predictive model. For example, ifpredictive model generator 210 has generated a predictive modelindicative of a relationship between a vegetative index value andmachine speed, then, given the vegetative index value at differentlocations across the field, predictive map generator 212 generates apredictive map 264 that predicts the target machine speed value atdifferent locations across the field. The vegetative index value,obtained from the vegetative index map, at those locations and therelationship between vegetative index value and machine speed, obtainedfrom the predictive model, are used to generate the predictive map 264.

Some variations in the data types that are mapped in the priorinformation map 258, the data types sensed by in-situ sensors 208, andthe data types predicted on the predictive map 264 will now bedescribed.

In some examples, the data type in the prior information map 258 isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a vegetative index map, and the variable sensed by the in-situsensors 208 may be yield. The predictive map 264 may then be apredictive yield map that maps predicted yield values to differentgeographic locations in the field. In another example, the priorinformation map 258 may be a vegetative index map, and the variablesensed by the in-situ sensors 208 may be crop height. The predictive map264 may then be a predictive crop height map that maps predicted cropheight values to different geographic locations in the field.

Also, in some examples, the data type in the prior information map 258is different from the data type sensed by in-situ sensors 208, and thedata type in the predictive map 264 is different from both the data typein the prior information map 258 and the data type sensed by the in-situsensors 208. For instance, the prior information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be crop height. The predictive map 264 may then be a predictivebiomass map that maps predicted biomass values to different geographiclocations in the field. In another example, the prior information map258 may be a vegetative index map, and the variable sensed by thein-situ sensors 208 may be yield. The predictive map 264 may then be apredictive speed map that maps predicted harvester speed values todifferent geographic locations in the field.

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a seed population map generated during planting, and the variablesensed by the in-situ sensors 208 may be stalk size. The predictive map264 may then be a predictive stalk size map that maps predicted stalksize values to different geographic locations in the field. In anotherexample, the prior information map 258 may be a seeding hybrid map, andthe variable sensed by the in-situ sensors 208 may be crop state such asstanding crop or down crop. The predictive map 264 may then be apredictive crop state map that maps predicted crop state values todifferent geographic locations in the field.

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type is the sameas the data type sensed by in-situ sensors 208, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 208. For instance, the prior information map 258 may bea yield map generated during a previous year, and the variable sensed bythe in-situ sensors 208 may be yield. The predictive map 264 may then bea predictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced prior information map 258 fromthe prior year can be used by predictive model generator 210 to generatea predictive model that models a relationship between the relative yielddifferences on the prior information map 258 and the yield values sensedby in-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 210 togenerate a predictive yield map.

In another example, the prior information map 258 may be a weedintensity map generated during a prior operation, such as from asprayer, and the variable sensed by the in-situ sensors 208 may be weedintensity. The predictive map 264 may then be a predictive weedintensity map that maps predicted weed intensity values to differentgeographic locations in the field. In such an example, a map of the weedintensities at time of spraying is geo-referenced recorded and providedto agricultural harvester 100 as an information map 258 of weedintensity. In-situ sensors 208 can detect weed intensity at geographiclocations in the field and predictive model generator 210 may then builda predictive model that models a relationship between weed intensity attime of harvest and weed intensity at time of spraying. This is becausethe sprayer will have impacted the weed intensity at time of spraying,but weeds may still crop up in similar areas again by harvest. However,the weed areas at harvest are likely to have different intensity basedon timing of the harvest, weather, weed type, among other things.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of an area, such as a field,for which a control parameter corresponding to the control zone forcontrolling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating agricultural harvester 100 orboth. In other examples, the control zones may be presented to theoperator 260 and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a weed typedisplayed on the map, based on the operator's observation. Settingscontroller 232 can generate control signals to control various settingson the agricultural harvester 100 based upon predictive map 264, thepredictive control zone map 265, or both. For instance, settingscontroller 232 can generate control signals to control machine andheader actuators 248. In one example, settings controller 232 cancontrol a sensitivity setting which controls the responsiveness ofcontrol system 214 in controlling the position (such as height, tilt, orroll), in response to header position error, to comply with a headerposition setting such as a header height setting, a header tilt setting,or a header. In response to the generated control signals, the machineand header actuators 248 operate to control, for example, one or more ofthe sieve and chaffer settings, concave clearance, rotor settings,cleaning fan speed settings, header height, header functionality, reelspeed, reel position, draper functionality (where agricultural harvester100 is coupled to a draper header), such as draper belt speed, cornheader functionality, internal distribution control and other actuators248 that affect the other functions of the agricultural harvester 100.In some examples, machine and header actuators 248 can be controlled toadjust a back shaft speed (also referred to as the header drive speed).For instance, in the example of a corn header, the back shaft speed canbe adjusted to control the speed of one or more of stalk rolls,gathering chains, and an auger on the corn header. In some examples,machine and header actuators 248 can include a rotatable outputmechanism, such as a drive shaft, the output of which can be controlledto control the back shaft speed. Path planning controller 234illustratively generates control signals to control steering subsystem252 to steer agricultural harvester 100 according to a desired path.Path planning controller 234 can control a path planning system togenerate a route for agricultural harvester 100 and can controlpropulsion subsystem 250 and steering subsystem 252 to steeragricultural harvester 100 along that route. Feed rate controller 236may receive a variety of different inputs indicative of a feed rate ofmaterial through agricultural harvester 100 and can control varioussubsystems, such as propulsion subsystem 250 and machine actuators 248,to control the feed rate based upon the predictive map 264 or predictivecontrol zone map 265 or both. For instance, as agricultural harvester100 approaches a weed patch having an intensity value above a selectedthreshold, feed rate controller 236 may generate a control signal tocontrol propulsion subsystem 252 to reduce the speed of agriculturalharvester 100 to maintain constant feed rate of biomass through theagricultural harvester 100. Header and reel controller 238 can generatecontrol signals to control a header or a reel or other headerfunctionality. Draper belt controller 240 can generate control signalsto control a draper belt or other draper functionality based upon thepredictive map 264, predictive control zone map 265, or both. Deck plateposition controller 242 can generate control signals to control aposition of a deck plate included on a header based on predictive map264 or predictive control zone map 265 or both, and residue systemcontroller 244 can generate control signals to control a residuesubsystem 138 based upon predictive map 264 or predictive control zonemap 265, or both. Machine cleaning controller 245 can generate controlsignals to control machine cleaning subsystem 254. For instance, basedupon the different types of seeds or weeds passed through agriculturalharvester 100, a particular type of machine cleaning operation or afrequency with which a cleaning operation is performed may becontrolled. Other controllers included on the agricultural harvester 100can control other subsystems based on the predictive map 264 orpredictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon prior information map 258.

At 280, agricultural harvester 100 receives prior information map 258.Examples of prior information map 258 or receiving prior information map258 are discussed with respect to blocks 281, 282, 284 and 286. Asdiscussed above, prior information map 258 maps values of a variable,corresponding to a first characteristic, to different locations in thefield, as indicated at block 282. As indicated at block 281, receivingthe prior information map 258 may involve selecting one or more of aplurality of possible prior information maps that are available. Forinstance, one prior information map may be a vegetative index mapgenerated from aerial imagery. Another prior information map may be amap generated during a prior pass through the field which may have beenperformed by a different machine performing a previous operation in thefield, such as a sprayer or a planting machine or seeding machine orother machine. The process by which one or more prior information mapsare selected can be manual, semi-automated, or automated. The priorinformation map 258 is based on data collected prior to a currentharvesting operation. This is indicated by block 284. For instance, thedata may be collected based on aerial images taken during a previousyear, or earlier in the current growing season, or at other times. Thedata may be based on data detected in ways other than using aerialimages. For instance, agricultural harvester 100 may be fitted with asensor, such as an internal optical sensor, that identifies weed seedsor other types of material exiting agricultural harvester 100. The weedseed or other data detected by the sensor during a previous year'sharvest may be used as data used to generate the prior information map258. The sensed weed data or other data may be combined with other datato generate the prior information map 258. For example, based upon amagnitude of the weed seeds exiting agricultural harvester 100 atdifferent locations and based upon other factors, such as whether theseeds are being spread by a spreader or dropped in a windrow; theweather conditions, such as wind, when the seeds are being dropped orspread; drainage conditions which may move seeds around in the field; orother information, the location of those weed seeds can be predicted sothat the prior information map 258 maps the predicted seed locations inthe field. The data for the prior information map 258 can be transmittedto agricultural harvester 100 using communication system 206 and storedin data store 202. The data for the prior information map 258 can beprovided to agricultural harvester 100 using communication system 206 inother ways as well, and this is indicated by block 286 in the flowdiagram of FIG. 3. In some examples, the prior information map 258 canbe received by communication system 206.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of a characteristic, such as a speed characteristic, asindicated by block 288. Examples of in-situ sensors 288 are discussedwith respect to blocks 222, 290, and 226. As explained above, thein-situ sensors 208 include on-board sensors 222; remote in-situ sensors224, such as UAV-based sensors flown at a time to gather in-situ data,shown in block 290; or other types of in-situ sensors, designated byin-situ sensors 226. In some examples, data from on-board sensors isgeoreferenced using position, heading, or speed data from geographicposition sensor 204.

Predictive model generator 210 controls the prior informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theprior information map 258 and the in-situ values sensed by the in-situsensors 208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the prior information map 258 andthe in-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the prior information map 258, asindicated by block 294.

It should be noted that, in some examples, the prior information map 258may include two or more different maps or two or more different maplayers of a single map. Each map layer may represent a different datatype from the data type of another map layer or the map layers may havethe same data type that were obtained at different times. Each map inthe two or more different maps or each layer in the two or moredifferent map layers of a map maps a different type of variable to thegeographic locations in the field. In such an example, predictive modelgenerator 210 generates a predictive model that models the relationshipbetween the in-situ data and each of the different variables mapped bythe two or more different maps or the two or more different map layers.Similarly, the in-situ sensors 208 can include two or more sensors eachsensing a different type of variable. Thus, the predictive modelgenerator 210 generates a predictive model that models the relationshipsbetween each type of variable mapped by the prior information map 258and each type of variable sensed by the in-situ sensors 208. Predictivemap generator 212 can generate a functional predictive map 263 thatpredicts a value for each sensed characteristic sensed by the in-situsensors 208 (or a characteristic related to the sensed characteristic)at different locations in the field being harvested using the predictivemodel and each of the maps or map layers in the prior information map258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 295, 299and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 intocontrol zones based on the values on the predictive map 264.Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system, or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system 214, the controllable subsystems 216, based on wearconsiderations, or on other criteria as indicated by block 295.Predictive map generator 212 configures predictive map 264 forpresentation to an operator or other user. Control zone generator 213can configure predictive control zone map 265 for presentation to anoperator or other user. This is indicated by block 299. When presentedto an operator or other user, the presentation of the predictive map 264or predictive control zone map 265 or both may contain one or more ofthe predictive values on the predictive map 264 correlated to geographiclocation, the control zones on predictive control zone map 265correlated to geographic location, and settings values or controlparameters that are used based on the predicted values on map 264 orzones on predictive control zone map 265. The presentation can, inanother example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on predictive map264 or the zones on predictive control zone map 265 conform to measuredvalues that may be measured by sensors on agricultural harvester 100 asagricultural harvester 100 moves through the field. Further whereinformation is presented to more than one location, an authenticationand authorization system can be provided to implement authentication andauthorization processes. For instance, there may be a hierarchy ofindividuals that are authorized to view and change maps and otherpresented information. By way of example, an on-board display device mayshow the maps in near real time locally on the machine, or the maps mayalso be generated at one or more remote locations, or both. In someexamples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display markers are visible on thephysical display device and which values the corresponding person maychange. As an example, a local operator of machine 100 may be unable tosee the information corresponding to the predictive map 264 or make anychanges to machine operation. A supervisor, such as a supervisor at aremote location, however, may be able to see the predictive map 264 onthe display but be prevented from making any changes. A manager, who maybe at a separate remote location, may be able to see all of the elementson predictive map 264 and also be able to change the predictive map 264.In some instances, the predictive map 264 accessible and changeable by amanager located remotely may be used in machine control. This is oneexample of an authorization hierarchy that may be implemented. Thepredictive map 264 or predictive control zone map 265 or both can beconfigured in other ways as well, as indicated by block 297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Particularly, atblock 300, control system 214 detects an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated and the controllable subsystems 216 that are controlled andthe timing of the control signals can be based on various latencies ofcrop flow through the agricultural harvester 100 and the responsivenessof the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of apredictive speed map can be used to control one or more subsystems 216.For instance, the predictive speed map can include speed valuesgeoreferenced to locations within the field being harvested. The speedvalues from the predictive speed map can be extracted and used tocontrol the propulsion subsystem 250. By controlling the propulsionsubsystem 250, a feed rate of material moving through the agriculturalharvester 100 can be controlled. Similarly, the header height can becontrolled to take in more or less material, and, thus, the headerheight can also be controlled to control feed rate of material throughthe agricultural harvester 100. In other examples, if the predictive map264 maps weed height relative to positions in the field, control of theheader height can be implemented. For example, if the values present inthe predictive weed map indicate one or more areas having weed heightwith a first height amount, then header and reel controller 238 cancontrol the header height so that the header is positioned above thefirst height amount of the weeds within the one or more areas havingweeds at the first height amount when performing the harvestingoperation. Thus, the header and reel controller 238 can be controlledusing georeferenced values present in the predictive weed map toposition the header to a height that is above the predicted heightvalues of weeds obtained from the predictive weed map. Further, theheader height can be changed automatically by the header and reelcontroller 238 as the agricultural harvester 100 proceeds through thefield using georeferenced values obtained from the predictive weed map.The preceding example involving weed height and intensity using apredictive weed map is provided merely as an example. Consequently, awide variety of other control signals can be generated using valuesobtained from a predictive speed map or other type of predictive map tocontrol one or more of the controllable subsystems 216. In someexamples, the backshaft speed, reel speed, draper speed, or headerheight sensitivities can be controlled based on a predictive speed map.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed, theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322 and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold trigger or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in prior information map258) are within a selected range or is less than a defined amount, orbelow a threshold value, then a new predictive model is not generated bythe predictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data are outside of the selected range, are greater than thedefined amount, or are above the threshold value, for example, then thepredictive model generator 210 generates a new predictive model usingall or a portion of the newly received in-situ sensor data that thepredictive map generator 212 uses to generate a new predictive map 264.At block 320, variations in the in-situ sensor data, such as a magnitudeof an amount by which the data exceeds the selected range or a magnitudeof the variation of the relationship between the in-situ sensor data andthe information in the prior information map 258, can be used as atrigger to cause generation of a new predictive model and predictivemap. Keeping with the examples described above, the threshold, therange, and the defined amount can be set to default values; set by anoperator or user interaction through a user interface; set by anautomated system; or set in other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different prior informationmap (different from the originally selected prior information map 258),then switching to the different prior information map may triggerrelearning by predictive model generator 210, predictive map generator212, control zone generator 213, control system 214, or other items. Inanother example, transitioning of agricultural harvester 100 to adifferent topography or to a different control zone may be used aslearning trigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264, change a size, shape, position, or existence ofa control zone on predictive control zone map 265, or both. Block 321shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide a manualadjustment to the controllable subsystem reflecting that the operator260 desires the controllable subsystem to operate in a different waythan is being commanded by control system 214. Thus, manual alterationof a setting by the operator 260 can cause one or more of predictivemodel generator 210 to relearn a model, predictive map generator 212 toregenerate map 264, control zone generator 213 to regenerate one or morecontrol zones on predictive control zone map 265, and control system 214to relearn a control algorithm or to perform machine learning on one ormore of the controller components 232 through 246 in control system 214based upon the adjustment by the operator 260, as shown in block 322.Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213, and control system 214performs machine learning to generate a new predictive model, a newpredictive map, a new control zone, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving aninformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4 is a block diagram of a portion of the agricultural harvester 100shown in FIG. 1. Particularly, FIG. 4 shows, among other things,examples of the predictive model generator 210 and the predictive mapgenerator 212 in more detail. FIG. 4 also illustrates information flowamong the various components shown. The predictive model generator 210receives prior information map 258, which may be a vegetative index map332, a predictive yield map 333, a biomass map 335, a crop state map337, a topographic map 339, a soil property map 341 or a seeding map 343as an information map. Predictive model generator 210 also receives ageographic location 334, or an indication of a geographic location, fromgeographic position sensor 204. In-situ sensors 208 illustrativelyinclude machine speed sensor 146, or a sensor 336 that senses an outputfrom feed rate controller 236, as well as a processing system 338. Theprocessing system 338 processes sensor data generated from machine speedsensor 146 or from sensor 336, or both, to generate processed data, someexamples of which are described below.

In some examples, sensor 336 may be a sensor, that generates a signalindicative of the control outputs from feed rate controller 236. Thecontrol signals may be speed control signals or other control signalsthat are applied to controllable subsystems 216 to control feed rate ofmaterial through agricultural harvester 100. Processing system 338processes the signals obtained via the sensor 336 to generate processeddata 340 identifying the speed of agricultural harvester 100. Processeddata 340 may include a location of agricultural harvester 100corresponding to the speed of agricultural harvester 100.

In some examples, raw or processed data from in-situ sensor(s) 208 maybe presented to operator 260 via operator interface mechanism 218.Operator 260 may be onboard the agricultural harvester 100 or at aremote location.

The present discussion proceeds with respect to an example in whichin-situ sensor 208 is machine speed sensor 146. It will be appreciatedthat this is just one example, and the sensors mentioned above, as otherexamples of in-situ sensor 208 from which machine speed can be derivedare contemplated herein as well. As shown in FIG. 4, the examplepredictive model generator 210 includes one or more of a vegetativeindex (VI) value-to-speed model generator 342, a biomass-to-speed modelgenerator 344, topography-to-speed model generator 345, yield-to-speedmodel generator 347, crop state-to-speed model generator 349, soilproperty-to-speed model generator 351 and a seedingcharacteristic-to-speed model generator 346. In other examples, thepredictive model generator 210 may include additional, fewer, ordifferent components than those shown in the example of FIG. 4.Consequently, in some examples, the predictive model generator 210 mayinclude other items 348 as well, which may include other types ofpredictive model generators to generate other types of models.

Model generator 342 identifies a relationship between machine speeddetected in processed data 340, at a geographic location correspondingto where the processed data 340 were obtained, and vegetative indexvalues from the vegetative index map 332 corresponding to the samelocation in the field where the weed characteristic was detected. Basedon this relationship established by model generator 342, model generator342 generates a predictive speed model. The predictive speed model isused by speed map generator 352 to predict target machine speed atdifferent locations in the field based upon the georeferenced vegetativeindex values contained in the vegetative index map 332 at the samelocations in the field.

Model generator 344 identifies a relationship between machine speedrepresented in the processed data 340, at a geographic locationcorresponding to the processed data 340, and the biomass value at thesame geographic location. Again, the biomass value is the georeferencedvalue contained in the biomass map 335. Model generator 344 thengenerates a predictive speed model that is used by speed map generator354 to predict the target machine speed at a location in the field basedupon the biomass value for that location in the field.

Model generator 346 identifies a relationship between the machine speedidentified by processed data 340 at a particular location in the fieldand the seeding characteristic value from the seeding characteristic map343 at that same location. Model generator 346 generates a predictivespeed model that is used by speed map generator 356 to predict thetarget machine speed at a particular location in the field based uponthe seeding characteristic value at that location in the field.

In light of the above, the predictive model generator 210 is operable toproduce a plurality of predictive speed models, such as one or more ofthe predictive speed models generated by model generators 342, 344, 345,346, 347, 349 and 351. In another example, two or more of the predictivespeed models described above may be combined into a single predictivespeed model that predicts target machine speed based on two or more ofthe vegetative index value the biomass value, the topography, the yield,the seeding characteristic, the crop state, or the soil property, atdifferent locations in the field. Any of these speed models, orcombinations thereof, are represented collectively by predictive model350 in FIG. 4.

The predictive model 350 is provided to predictive map generator 212. Inthe example of FIG. 4, predictive map generator 212 includes a speed mapgenerator 352. In other examples, the predictive map generator 212 mayinclude additional, fewer, or different map generators. Thus, in someexamples, the predictive map generator 212 may include other items 358which may include other types of map generators to generate speed maps.Speed map generator 352 receives the predictive model 350, whichpredicts target machine speed based upon a value from one or more priorinformation maps 258, along with the one or more prior information maps258, and generates a predictive map that predicts the target machinespeed at different locations in the field.

Predictive map generator 212 outputs one or more functional predictivespeed maps 360 that are predictive of one or more of target machinespeed. The functional predictive speed map 360 predicts the targetmachine speed at different locations in a field. The functionalpredictive speed maps 360 may be provided to control zone generator 213,control system 214, or both. Control zone generator 213 generatescontrol zones and incorporates those control zones into the functionalpredictive map, i.e., predictive map to produce predictive control zonemap 265. One or both of predictive map 264 and predictive control zonemap 265 may be provided to control system 214, which generates controlsignals to control one or more of the controllable subsystems 216, suchas propulsion subsystem 250 based upon the predictive map 264,predictive control zone map 265, or both.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive model 350 and the functional predictive speed map 360. Atblock 362, predictive model generator 210 and predictive map generator212 receive an information map 258. The prior information map 258 may beany of the maps 332, 333, 335, 337, 339, 341 or 343. In addition, block361 indicates that the received prior information map may be a singlemap. Block 363 indicates that the prior information map may be multiplemaps or multiple map layers. Block 365 indicates that the priorinformation map 258 may take other forms as well. At block 364,processing system 338 receives one or more signals from machine speedsensor 146 or sensor 336 or both.

At block 372, processing system 338 processes the one or more receivedsignals to generate processed data 340 indicative of a machine speed ofagricultural harvester 100.

At block 382, predictive model generator 210 also obtains the geographiclocation 334 corresponding to the processed data. For instance, thepredictive model generator 210 can obtain the geographic position fromgeographic position sensor 204 and determine, based upon machine delays,machine speed, etc., a precise geographic location where the processeddata was taken or from which the processed data 340 was derived.

At block 384, predictive model generator 210 generates one or morepredictive models, such as predictive model 350, that model arelationship between a value in the one or more prior information map258, and a machine speed being sensed by the in-situ sensor 208. VIvalue-to-speed model generator 342 generates a predictive model thatmodels a relationship between VI values in VI map 332 and machine speedsensed by in-situ sensor 208. Biomass-to-speed model generator 344generates a predictive model that models a relationship between biomassvalues in biomass map 335 and machine speed sensed by in-situ sensor208. Topography-to-speed model generator 345 generates a predictivemodel that models a relationship between one or more topography valuessuch as pitch, roll, or slope in topographic map 339 and machine speedsensed by in-situ sensor 208. Seeding characteristic-to-speed modelgenerator 346 generates a predictive model that models a relationshipbetween seeding characteristics in seeding map 343 and machine speedsensed by in-situ sensor 208. Yield-to-speed model generator 347generates a predictive model that models a relationship between yieldvalues in yield map 333 and machine speed sensed by in-situ sensor 208.Crop state-to-speed model generator 342 generates a predictive modelthat models a relationship between crop state values in crop state map337 and machine speed sensed by in-situ sensor 208. Soilproperty-to-speed model generator 351 generates a predictive model thatmodels a relationship between soil property values in soil property map341 and machine speed sensed by in-situ sensor 208. At block 386, thepredictive model 350 is provided to predictive map generator 212 whichgenerates a functional predictive speed map 360 that maps a predicted,target machine speed based on the prior information map 258 and thepredictive speed model 350. Speed map generator 352 may generate thefunctional predictive speed map 360 using a predictive model 350 thatmodels a relationship between VI values in VI map 332 and machine speedand using the VI map 332. Speed map generator 352 may generate thefunctional predictive speed map 360 using a predictive model 350 thatmodels a relationship between yield values in yield map 333 and machinespeed and using the yield map 333. Speed map generator 352 may generatethe functional predictive speed map 360 using a predictive model 350that models a relationship between biomass values in biomass map 335 andmachine speed and using the biomass map 335. Speed map generator 352 maygenerate the functional predictive speed map 360 using a predictivemodel 350 that models a relationship between crop state values in cropstate map 337 and machine speed and using the crop state map 337. Speedmap generator 352 may generate the functional predictive speed map 360using a predictive model 350 that models a relationship betweentopographic values in topographic map 339 and machine speed and usingthe topographic map 332. Speed map generator 352 may generate thefunctional predictive speed map 360 using a predictive model 350 thatmodels a relationship between soil property values in soil property map341 and machine speed and using the soil property map 341. Speed mapgenerator 352 may generate the functional predictive speed map 360 usinga predictive model 350 that models a relationship between seedingcharacteristic values in seeding map 343 and machine speed and using theseeding map 343.

Thus, as an agricultural harvester is moving through a field performingan agricultural operation, one or more functional predictive speed maps360 are generated as the agricultural operation is being performed.

At block 394, predictive map generator 212 outputs the functionalpredictive speed map 360. At block 391 functional predictive speed mapgenerator 212 outputs the functional predictive speed map 360 forpresentation to and possible interaction by operator 260. At block 393,predictive map generator 212 may configure the map 360 for consumptionby control system 214. At block 395, predictive map generator 212 canalso provide the map 360 to control zone generator 213 for generation ofcontrol zones. At block 397, predictive map generator 212 configures themap 360 in other ways as well. The functional predictive speed map 360(with or without the control zones) is provided to control system 214.At block 396, control system 214 generates control signals to controlthe controllable subsystems 216 based upon the functional predictivespeed map 360. The control system 214 may control propulsion subsystem250 or other subsystems 399.

FIG. 6 is a block diagram of an example portion of the agriculturalharvester 100 shown in FIG. 1. Particularly, FIG. 6 shows, among otherthings, examples of predictive model generator 210 and predictive mapgenerator 212. In the illustrated example, the information map 259 isone or more of a functional predictive speed map 360, a speed map withcontrol zones 400, or another speed map 401. Information map 259 may bea prior information map, such as information map 258, or a predictivemap, such as a predictive map generated during the operation of theagricultural harvester 100.

Also, in the example shown in FIG. 6, in-situ sensor 208 can include oneor more of a variety of different sensors 402 and processing system 406.Some examples of in-situ sensors 208 and sensors 402 are described belowwith respect to FIG. 8. Sensors 402 can sense any number of a variety ofagricultural characteristics or values indicative of any number of avariety of agricultural characteristics. Thus, in some examples, sensordata generated by sensors 402 can be indicative of an agriculturalcharacteristic, or can be used to derive an agricultural characteristic.Thus, in some examples, a relationship between the sensor data and othercharacteristics or characteristic values can be identified and modeled.In other examples, the sensor data may be used as an indicator ofanother characteristic or characteristic value, and thus a relationshipbetween the characteristic or characteristic value (indicated by thesensor data) and other characteristics or characteristics can beidentified and modeled. Thus, as used herein, sensor data can refer tothe sensor data itself or can refer to the various characteristics orcharacteristics that can be indicated by the sensor data.

In one example, sensors 402 can include an operator input sensor thatsenses various operator inputs. The inputs can be setting inputs forcontrolling the settings on agricultural harvester 100 or other controlinputs, such as steering inputs and other inputs. Thus, when an operatorof agricultural harvester changes a setting or provides a commandedinput, such as through an operator interface mechanism 218, such aninput is detected by the operator input sensor which provides a sensorsignal indicative of that sensed operator input. This is merely oneexample of one of the variety of different sensors 402.

Predictive model generator 210 may include speedcharacteristic-to-in-situ sensor data model generator 416. In otherexamples, predictive model generator 210 can include additional, fewer,or other model generators 424. Predictive model generator 210 mayreceive a geographic location indicator 324 from geographic positionsensor 204 and generate a predictive model 426 that models arelationship between the information in one or more of the informationmaps and one or more of the in-situ sensors 402. For instance,speed-to-in-situ sensor data model generator 416 generates arelationship between speed characteristic values (which may be on map360, on map 400, or on map 401) and the values sensed by sensor 402.Speed-to-in-situ data model generator 416 illustratively generates amodel that represents a relationship between the travel speed orvariable indicative of travel speed in information map 259 and thecharacteristic sensed by in-situ sensor 402. Predictive sensor datamodel 426 generated by the predictive model generator 210 can includethe predictive model that may be generated by speed-to-in-situ sensordata model generator 416.

In the example of FIG. 6, predictive map generator 212 includespredictive sensor data map generator 428. In other examples, predictivemap generator 212 can include additional, =or other map generators 434.Predictive sensor data map generator 428 receives a predictive model 426that models the relationship between machine speed on the informationmaps 259 and the characteristic sensed by sensor 402. Predictive sensordata map generator 428 generates a functional predictive sensor data map436 that predicts sensor data at different locations in the field basedupon the machine speed in one or more of the information maps 259 atthose locations in the field and based on predictive model 426.

Predictive map generator 212 outputs the functional predictive sensordata map 436. The functional predictive sensor data map 436 may beprovided to control zone generator 213, control system 214, or both.Control zone generator 213 generates and incorporates control zones toprovide a functional predictive sensor data map 436 with control zones.The functional predictive sensor data map 436 (with or without controlzones) may be provided to control system 214, which generates controlsignals to control one or more of the controllable subsystems 216 basedupon the functional predictive sensor data map 436 (with or withoutcontrol zones). The functional predictive sensor data map 436 (with orwithout control zones) may be presented to operator 260 or another user.

In one example, sensor 402, such as an operator input sensor, senses anoperator input indicative of a commanded back shaft speed setting (orheader drive speed) of agricultural harvester 100 to control, forexample, the speed at which one or more stalk rolls, one or moregathering chains, or one or more augers on a corn header operate. Thus,in such an example, speed characteristic-to-in-situ sensor data modelgenerator 416 models a relationship between speed values from one ormore of the maps (functional predictive speed map 360, speed map withcontrol zones 400, or other speed map 401) and back shaft speed settingsas indicated by detected operator inputs and predictive sensor data mapgenerator 428 generates a functional predictive sensor data map thatpredicts values of back shaft speed settings at different locationsacross the field based on the speed values, from one or more of themaps, at those locations. This is merely an example, and various othermachine settings indicated by operator input commands can also besimilarly modeled and predictively mapped, for instance, draper beltspeed settings, reel speed settings, and header sensitivity settings.

FIG. 7 shows a flow diagram illustrating one example of the operation ofpredictive model generator 210 and predictive map generator 212 ingenerating predictive model 426 and functional predictive sensor datamap 436. At block 442, predictive model generator 210 and predictive mapgenerator 212 receive an information map. The information map may befunctional predictive speed map 360, speed map with control zones 400,or another speed map 406. At block 444, sensor 402 generates a sensorsignal containing sensor data indicative of the characteristic sensed byin-situ sensor 402. The in-situ sensor 402 can be, for example, one ormore of the sensors described below with respect to FIG. 8.

At block 454, processing system 406 processes the data contained in thesensor signal received from the in-situ sensor 402 to obtain processeddata 409, shown in FIG. 6. The data contained in the sensor signal canbe in a raw format that is processed to receive processed data 409. Forexample, a temperature sensor signal includes electrical resistancedata. This electrical resistance data can be processed into temperaturedata. In other examples, processing may comprise digitizing, encoding,formatting, scaling, filtering, or classifying data.

Returning to FIG. 7, at block 456, predictive model generator 210 alsoreceives a geographic location 334 from geographic position sensor 204,as shown in FIG. 6. The geographic location 334 may be correlated to thegeographic location from which the sensed variable or variables, sensedby in-situ sensors 402, were taken. For instance, the predictive modelgenerator 210 can obtain the geographic location 334 from geographicposition sensor 204 and determine, based upon machine delays, machinespeed, etc., a precise geographic location from which the processed data409 was derived.

At block 458, predictive model generator 210 generates a predictivemodel 426 that model a relationship between a mapped speed value in aninformation map and a characteristic represented in the processed data409 or a related characteristic, such as a characteristic thatcorrelates to the characteristic sensed by in-situ sensor 402.

The predictive sensor data model 426 is provided to predictive mapgenerator 212. At block 466, predictive map generator 212 generates afunctional predictive sensor data map 436. The functional predictivesensor data map 436 predicts sensor data values that will be generatedby in-situ sensor 402 on agricultural harvester 100 at differentlocations in the field. Thus, as agricultural harvester 100 is movingthrough a field performing an agricultural operation, the functionalpredictive senor data map 436 is generated as the agricultural operationis being performed.

At block 468, predictive map generator 212 outputs functional predictivesensor data map 436. At block 470, predictive map generator 212 mayconfigure the map for presentation to and possible interaction by anoperator 260 or another user. At block 472, predictive map generator 212may configure the map for consumption by control system 214. At block474, predictive map generator 212 can provide the functional predictivesensor data map 436 to control zone generator 213 for generation andincorporation of control zones. At block 476, predictive map generator212 configures the functional predictive sensor data map 436 in otherways. Functional predictive sensor data map 436 (with or without controlzones) may be presented to operator 260 or another user or provided tocontrol system 214 as well.

At block 478, control system 214 then generates control signals tocontrol one or more of the controllable subsystems based upon thefunctional predictive sensor data map 436 (or the functional predictivesensor data map 436 having control zones) as well as an input from thegeographic position sensor 204. For example, when the functionalpredictive sensor data map 436 or functional predictive sensor data map436 containing control zones is provided to control system 214.Header/reel controller 238 or other controllers 246, in response,generate control signals to control the machine/header actuator 248which may also include actuators for other front end equipment.

In another example in which control system 214 receives the functionalpredictive sensor data map 436 or functional predictive sensor data map436 with control zones added, the settings controller 232 controlspropulsion subsystem 250 (shown as one of the controllable subsystems216 in FIG. 2).

In another example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the path planning controller 234controls steering subsystem 252 to steer agricultural harvester 100. Inanother example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the residue system controller 244controls residue subsystem 138. In another example in which controlsystem 214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thesettings controller 232 controls thresher settings of thresher 110. Inanother example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the settings controller 232 or anothercontroller 246 controls material handling subsystem 125. In anotherexample in which control system 214 receives the functional predictivesensor data map 436 or the functional predictive sensor data map 436with control zones added, the settings controller 232 controls cropcleaning subsystem. In another example in which control system 214receives the functional predictive sensor data map 436 or the functionalpredictive sensor data map 436 with control zones added, the machinecleaning controller 245 controls machine cleaning subsystem 254 onagricultural harvester 100. In another example in which control system214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thecommunication system controller 229 controls communication system 206.In another example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the operator interface controller 231controls operator interface mechanisms 218 on agricultural harvester100. In another example in which control system 214 receives thefunctional predictive sensor data map 436 or the functional predictivesensor data map 436 with control zones added, the deck plate positioncontroller 242 controls machine/header actuators to control a deck plateon agricultural harvester 100. In another example in which controlsystem 214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thedraper belt controller 240 controls machine/header actuators to controla draper belt on agricultural harvester 100. In another example in whichcontrol system 214 receives the functional predictive sensor data map436 or the functional predictive sensor data map 436 with control zonesadded, the other controllers 246 control other controllable subsystems256 on agricultural harvester 100.

FIG. 8 is a block diagram showing some examples of real-time (in-situ)sensors 208. Some of the sensors shown in FIG. 8, or differentcombinations of them, may have both a sensor 402 and a processing system406, while others may act as sensor 402 described above with respect toFIGS. 6 and 7 where the processing system 406 is separate. Some of thepossible in-situ sensors 208 shown in FIG. 8 are shown and describedabove with respect to previous FIGS. 1-7, and are similarly numbered.FIG. 8 shows that in-situ sensors 208 can include operator input sensors480, machine sensors 482, harvested material property sensors 484, fieldand soil property sensors 485, environmental characteristic sensors 487,and they may include a wide variety of other sensors 226. Operator inputsensors 480 may be sensors that sense operator inputs through operatorinterface mechanisms 218. Therefore, operator input sensors 480 maysense user movement of linkages, joysticks, a steering wheel, buttons,dials, or pedals. Operator input sensors 480 can also sense userinteractions with other operator input mechanisms, such as with a touchsensitive screen, with a microphone where speech recognition isutilized, or any of a wide variety of other operator input mechanisms.

Machine sensors 482 may sense different characteristics of agriculturalharvester 100. For instance, as discussed above, machine sensors 482 mayinclude machine speed sensors 146, separator loss sensor 148, cleangrain camera 150, forward looking image capture mechanism 151, losssensors 152 or geographic position sensor 204, examples of which aredescribed above. Machine sensors 482 can also include machine settingsensors 491 that sense machine settings. Some examples of machinesettings were described above with respect to FIG. 1. Front-endequipment (e.g., header) position sensor 493 can sense the position ofthe header 102, reel 164, cutter 104, or other front-end equipmentrelative to the frame of agricultural harvester 100. For instance,sensors 493 may sense the height of header 102 above the ground. Machinesensors 482 can also include front-end equipment (e.g., header)orientation sensors 495. Sensors 495 may sense the orientation of header102 relative to agricultural harvester 100, or relative to the ground.Machine sensors 482 may include stability sensors 497. Stability sensors497 sense oscillation or bouncing motion (and amplitude) of agriculturalharvester 100. Machine sensors 482 may also include residue settingsensors 499 that are configured to sense whether agricultural harvester100 is configured to chop the residue, produce a windrow, or deal withthe residue in another way. Machine sensors 482 may include cleaningshoe fan speed sensor 551 that senses the speed of cleaning fan 120.Machine sensors 482 may include concave clearance sensors 553 that sensethe clearance between the rotor 112 and concaves 114 on agriculturalharvester 100. Machine sensors 482 may include chaffer clearance sensors555 that sense the size of openings in chaffer 122. The machine sensors482 may include threshing rotor speed sensor 557 that senses a rotorspeed of rotor 112. Machine sensors 482 may include rotor pressuresensor 559 that senses the pressure used to drive rotor 112. Machinesensors 482 may include sieve clearance sensor 561 that senses the sizeof openings in sieve 124. The machine sensors 482 may include MOGmoisture sensor 563 that senses a moisture level of the MOG passingthrough agricultural harvester 100. Machine sensors 482 may includemachine orientation sensor 565 that senses the orientation ofagricultural harvester 100. Machine sensors 482 may include materialfeed rate sensors 567 that sense the feed rate of material as thematerial travels through feeder house 106, clean grain elevator 130, orelsewhere in agricultural harvester 100. Machine sensors 482 can includebiomass sensors 569 that sense the biomass traveling through feederhouse 106, through separator 116, or elsewhere in agricultural harvester100. The machine sensors 482 may include fuel consumption sensor 571that senses a rate of fuel consumption over time of agriculturalharvester 100. Machine sensors 482 may include power utilization sensor573 that senses power utilization in agricultural harvester 100, such aswhich subsystems are utilizing power, or the rate at which subsystemsare utilizing power, or the distribution of power among the subsystemsin agricultural harvester 100. Machine sensors 482 may include tirepressure sensors 577 that sense the inflation pressure in tires 144 ofagricultural harvester 100. Machine sensor 482 may include a widevariety of other machine performance sensors, or machine characteristicsensors, indicated by block 575. The machine performance sensors andmachine characteristic sensors 575 may sense machine performance orcharacteristics of agricultural harvester 100.

Harvested material property sensors 484 may sense characteristics of thesevered crop material as the crop material is being processed byagricultural harvester 100. The crop properties may include such thingsas crop type, crop moisture, grain quality (such as broken grain), MOGlevels, grain constituents such as starches and protein, MOG moisture,and other crop material properties.

Field and soil property sensors 485 may sense characteristics of thefield and soil. The field and soil properties may include soil moisture,soil compactness, the presence and location of standing water, soiltype, and other soil and field characteristics.

Environmental characteristic sensors 487 may sense one or moreenvironmental characteristics. The environmental characteristics mayinclude such things as wind direction and wind speed, precipitation,fog, dust level or other obscurants, or other environmentalcharacteristics.

FIG. 9 shows a block diagram illustrating one example of control zonegenerator 213. Control zone generator 213 includes work machine actuator(WMA) selector 486, control zone generation system 488, and regime zonegeneration system 490. Control zone generator 213 may also include otheritems 492. Control zone generation system 488 includes control zonecriteria identifier component 494, control zone boundary definitioncomponent 496, target setting identifier component 498, and other items520. Regime zone generation system 490 includes regime zone criteriaidentification component 522, regime zone boundary definition component524, settings resolver identifier component 526, and other items 528.Before describing the overall operation of control zone generator 213 inmore detail, a brief description of some of the items in control zonegenerator 213 and the respective operations thereof will first beprovided.

Agricultural harvester 100, or other work machines, may have a widevariety of different types of controllable actuators that performdifferent functions. The controllable actuators on agriculturalharvester 100 or other work machines are collectively referred to aswork machine actuators (WMAs). Each WMA may be independentlycontrollable based upon values on a functional predictive map, or theWMAs may be controlled as sets based upon one or more values on afunctional predictive map. Therefore, control zone generator 213 maygenerate control zones corresponding to each individually controllableWMA or corresponding to the sets of WMAs that are controlled incoordination with one another.

WMA selector 486 selects a WMA or a set of WMAs for which correspondingcontrol zones are to be generated. Control zone generation system 488then generates the control zones for the selected WMA or set of WMAs.For each WMA or set of WMAs, different criteria may be used inidentifying control zones. For example, for one WMA, the WMA responsetime may be used as the criteria for defining the boundaries of thecontrol zones. In another example, wear characteristics (e.g., how mucha particular actuator or mechanism wears as a result of movementthereof) may be used as the criteria for identifying the boundaries ofcontrol zones. Control zone criteria identifier component 494 identifiesparticular criteria that are to be used in defining control zones forthe selected WMA or set of WMAs. Control zone boundary definitioncomponent 496 processes the values on a functional predictive map underanalysis to define the boundaries of the control zones on thatfunctional predictive map based upon the values in the functionalpredictive map under analysis and based upon the control zone criteriafor the selected WMA or set of WMAs.

Target setting identifier component 498 sets a value of the targetsetting that will be used to control the WMA or set of WMAs in differentcontrol zones. For instance, if the selected WMA is propulsion system250 and the functional predictive map under analysis is a functionalpredictive speed map 438, then the target setting in each control zonemay be a target speed setting based on speed values contained in thefunctional predictive speed map 238 within the identified control zone.

In some examples, where agricultural harvester 100 is to be controlledbased on a current or future location of the agricultural harvester 100,multiple target settings may be possible for a WMA at a given location.In that case, the target settings may have different values and may becompeting. Thus, the target settings need to be resolved so that only asingle target setting is used to control the WMA. For example, where theWMA is an actuator in propulsion system 250 that is being controlled inorder to control the speed of agricultural harvester 100, multipledifferent competing sets of criteria may exist that are considered bycontrol zone generation system 488 in identifying the control zones andthe target settings for the selected WMA in the control zones. Forinstance, different target settings for controlling machine speed may begenerated based upon, for example, a detected or predicted feed ratevalue, a detected or predictive fuel efficiency value, a detected orpredicted grain loss value, or a combination of these. However, at anygiven time, the agricultural harvester 100 cannot travel over the groundat multiple speeds simultaneously. Rather, at any given time, theagricultural harvester 100 travels at a single speed. Thus, one of thecompeting target settings is selected to control the speed ofagricultural harvester 100.

Therefore, in some examples, regime zone generation system 490 generatesregime zones to resolve multiple different competing target settings.Regime zone criteria identification component 522 identifies thecriteria that are used to establish regime zones for the selected WMA orset of WMAs on the functional predictive map under analysis. Somecriteria that can be used to identify or define regime zones include,for example, crop type or crop variety based on an as-planted map oranother source of the crop type or crop variety, weed type, weedintensity, or crop state, such as whether the crop is down, partiallydown or standing. Just as each WMA or set of WMAs may have acorresponding control zone, different WMAs or sets of WMAs may have acorresponding regime zone. Regime zone boundary definition component 524identifies the boundaries of regime zones on the functional predictivemap under analysis based on the regime zone criteria identified byregime zone criteria identification component 522.

In some examples, regime zones may overlap with one another. Forinstance, a crop variety regime zone may overlap with a portion of or anentirety of a crop state regime zone. In such an example, the differentregime zones may be assigned to a precedence hierarchy so that, wheretwo or more regime zones overlap, the regime zone assigned with agreater hierarchical position or importance in the precedence hierarchyhas precedence over the regime zones that have lesser hierarchicalpositions or importance in the precedence hierarchy. The precedencehierarchy of the regime zones may be manually set or may beautomatically set using a rules-based system, a model-based system, oranother system. As one example, where a downed crop regime zone overlapswith a crop variety regime zone, the downed crop regime zone may beassigned a greater importance in the precedence hierarchy than the cropvariety regime zone so that the downed crop regime zone takesprecedence.

In addition, each regime zone may have a unique settings resolver for agiven WMA or set of WMAs. Settings resolver identifier component 526identifies a particular settings resolver for each regime zoneidentified on the functional predictive map under analysis and aparticular settings resolver for the selected WMA or set of WMAs.

Once the settings resolver for a particular regime zone is identified,that settings resolver may be used to resolve competing target settings,where more than one target setting is identified based upon the controlzones. The different types of settings resolvers can have differentforms. For instance, the settings resolvers that are identified for eachregime zone may include a human choice resolver in which the competingtarget settings are presented to an operator or other user forresolution. In another example, the settings resolver may include aneural network or other artificial intelligence or machine learningsystem. In such instances, the settings resolvers may resolve thecompeting target settings based upon a predicted or historic qualitymetric corresponding to each of the different target settings. As anexample, an increased vehicle speed setting may reduce the time toharvest a field and reduce corresponding time-based labor and equipmentcosts but may increase grain losses. A reduced vehicle speed setting mayincrease the time to harvest a field and increase correspondingtime-based labor and equipment costs but may decrease grain losses. Whengrain loss or time to harvest is selected as a quality metric, thepredicted or historic value for the selected quality metric, given thetwo competing vehicle speed settings values, may be used to resolve thespeed setting. In some instances, the settings resolvers may be a set ofthreshold rules that may be used instead of, or in addition to, theregime zones. An example of a threshold rule may be expressed asfollows:

-   -   If predicted biomass values within 20 feet of the header of the        agricultural harvester 100 are greater that x (where x is a        selected or predetermined value), then use the target setting        value that is chosen based on feed rate over other competing        target settings, otherwise use the target setting value based on        grain loss over other competing target setting values.

The settings resolvers may be logical components that execute logicalrules in identifying a target setting. For instance, the settingsresolver may resolve target settings while attempting to minimizeharvest time or minimize the total harvest cost or maximize harvestedgrain or based on other variables that are computed as a function of thedifferent candidate target settings. A harvest time may be minimizedwhen an amount to complete a harvest is reduced to at or below aselected threshold. A total harvest cost may be minimized where thetotal harvest cost is reduced to at or below a selected threshold.Harvested grain may be maximized where the amount of harvested grain isincreased to at or above a selected threshold.

FIG. 10 is a flow diagram illustrating one example of the operation ofcontrol zone generator 213 in generating control zones and regime zonesfor a map that the control zone generator 213 receives for zoneprocessing (e.g., for a map under analysis).

At block 530, control zone generator 213 receives a map under analysisfor processing. In one example, as shown at block 532, the map underanalysis is a functional predictive map. For example, the map underanalysis may be one of the functional predictive maps 436, 437, 438, or440. Block 534 indicates that the map under analysis can be other mapsas well.

At block 536, WMA selector 486 selects a WMA or a set of WMAs for whichcontrol zones are to be generated on the map under analysis. At block538, control zone criteria identification component 494 obtains controlzone definition criteria for the selected WMAs or set of WMAs. Block 540indicates an example in which the control zone criteria are or includewear characteristics of the selected WMA or set of WMAs. Block 542indicates an example in which the control zone definition criteria areor include a magnitude and variation of input source data, such as themagnitude and variation of the values on the map under analysis or themagnitude and variation of inputs from various in-situ sensors 208.Block 544 indicates an example in which the control zone definitioncriteria are or include physical machine characteristics, such as thephysical dimensions of the machine, a speed at which differentsubsystems operate, or other physical machine characteristics. Block 546indicates an example in which the control zone definition criteria areor include a responsiveness of the selected WMA or set of WMAs inreaching newly commanded setting values. Block 548 indicates an examplein which the control zone definition criteria are or include machineperformance metrics. Block 550 indicates an example in which the controlzone definition criteria are or includes operator preferences. Block 552indicates an example in which the control zone definition criteria areor include other items as well. Block 549 indicates an example in whichthe control zone definition criteria are time based, meaning thatagricultural harvester 100 will not cross the boundary of a control zoneuntil a selected amount of time has elapsed since agricultural harvester100 entered a particular control zone. In some instances, the selectedamount of time may be a minimum amount of time. Thus, in some instances,the control zone definition criteria may prevent the agriculturalharvester 100 from crossing a boundary of a control zone until at leastthe selected amount of time has elapsed. Block 551 indicates an examplein which the control zone definition criteria are based on a selectedsize value. For example, a control zone definition criteria that isbased on a selected size value may preclude definition of a control zonethat is smaller than the selected size. In some instances, the selectedsize may be a minimum size.

At block 554, regime zone criteria identification component 522 obtainsregime zone definition criteria for the selected WMA or set of WMAs.Block 556 indicates an example in which the regime zone definitioncriteria are based on a manual input from operator 260 or another user.Block 558 illustrates an example in which the regime zone definitioncriteria are based on crop type or crop variety. Block 560 illustratesan example in which the regime zone definition criteria are based onweed type or weed intensity or both. Block 562 illustrates an example inwhich the regime zone definition criteria are based on or include cropstate. Block 564 indicates an example in which the regime zonedefinition criteria are or include other criteria as well.

At block 566, control zone boundary definition component 496 generatesthe boundaries of control zones on the map under analysis based upon thecontrol zone criteria. Regime zone boundary definition component 524generates the boundaries of regime zones on the map under analysis basedupon the regime zone criteria. Block 568 indicates an example in whichthe zone boundaries are identified for the control zones and the regimezones. Block 570 shows that target setting identifier component 498identifies the target settings for each of the control zones. Thecontrol zones and regime zones can be generated in other ways as well,and this is indicated by block 572.

At block 574, settings resolver identifier component 526 identifies thesettings resolver for the selected WMAs in each regime zone defined byregimes zone boundary definition component 524. As discussed above, theregime zone resolver can be a human resolver 576, an artificialintelligence or machine learning system resolver 578, a resolver 580based on predicted or historic quality for each competing targetsetting, a rules-based resolver 582, a performance criteria-basedresolver 584, or other resolvers 586.

At block 588, WMA selector 486 determines whether there are more WMAs orsets of WMAs to process. If additional WMAs or sets of WMAs areremaining to be processed, processing reverts to block 436 where thenext WMA or set of WMAs for which control zones and regime zones are tobe defined is selected. When no additional WMAs or sets of WMAs forwhich control zones or regime zones are to be generated are remaining,processing moves to block 590 where control zone generator 213 outputs amap with control zones, target settings, regime zones, and settingsresolvers for each of the WMAs or sets of WMAs. As discussed above, theoutputted map can be presented to operator 260 or another user; theoutputted map can be provided to control system 214; or the outputtedmap can be output in other ways.

FIG. 11 illustrates one example of the operation of control system 214in controlling agricultural harvester 100 based upon a map that isoutput by control zone generator 213. Thus, at block 592, control system214 receives a map of the worksite. In some instances, the map can be afunctional predictive map that may include control zones and regimezones, as represented by block 594. In some instances, the received mapmay be a functional predictive map that excludes control zones andregime zones. Block 596 indicates an example in which the received mapof the worksite can be a information map having control zones and regimezones identified on it. Block 598 indicates an example in which thereceived map can include multiple different maps or multiple differentmap layers. Block 610 indicates an example in which the received map cantake other forms as well.

At block 612, control system 214 receives a sensor signal fromgeographic position sensor 204. The sensor signal from geographicposition sensor 204 can include data that indicates the geographiclocation 614 of agricultural harvester 100, the speed 616 ofagricultural harvester 100, the heading 618 or agricultural harvester100, or other information 620. At block 622, zone controller 247 selectsa regime zone, and, at block 624, zone controller 247 selects a controlzone on the map based on the geographic position sensor signal. At block626, zone controller 247 selects a WMA or a set of WMAs to becontrolled. At block 628, zone controller 247 obtains one or more targetsettings for the selected WMA or set of WMAs. The target settings thatare obtained for the selected WMA or set of WMAs may come from a varietyof different sources. For instance, block 630 shows an example in whichone or more of the target settings for the selected WMA or set of WMAsis based on an input from the control zones on the map of the worksite.Block 632 shows an example in which one or more of the target settingsis obtained from human inputs from operator 260 or another user. Block634 shows an example in which the target settings are obtained from anin-situ sensor 208. Block 636 shows an example in which the one or moretarget settings is obtained from one or more sensors on other machinesworking in the same field either concurrently with agriculturalharvester 100 or from one or more sensors on machines that worked in thesame field in the past. Block 638 shows an example in which the targetsettings are obtained from other sources as well.

At block 640, zone controller 247 accesses the settings resolver for theselected regime zone and controls the settings resolver to resolvecompeting target settings into a resolved target setting. As discussedabove, in some instances, the settings resolver may be a human resolverin which case zone controller 247 controls operator interface mechanisms218 to present the competing target settings to operator 260 or anotheruser for resolution. In some instances, the settings resolver may be aneural network or other artificial intelligence or machine learningsystem, and zone controller 247 submits the competing target settings tothe neural network, artificial intelligence, or machine learning systemfor selection. In some instances, the settings resolver may be based ona predicted or historic quality metric, on threshold rules, or onlogical components. In any of these latter examples, zone controller 247executes the settings resolver to obtain a resolved target setting basedon the predicted or historic quality metric, based on the thresholdrules, or with the use of the logical components.

At block 642, with zone controller 247 having identified the resolvedtarget setting, zone controller 247 provides the resolved target settingto other controllers in control system 214, which generate and applycontrol signals to the selected WMA or set of WMAs based upon theresolved target setting. For instance, where the selected WMA is amachine or header actuator 248, zone controller 247 provides theresolved target setting to settings controller 232 or header/realcontroller 238 or both to generate control signals based upon theresolved target setting, and those generated control signals are appliedto the machine or header actuators 248. At block 644, if additional WMAsor additional sets of WMAs are to be controlled at the currentgeographic location of the agricultural harvester 100 (as detected atblock 612), then processing reverts to block 626 where the next WMA orset of WMAs is selected. The processes represented by blocks 626 through644 continue until all of the WMAs or sets of WMAs to be controlled atthe current geographical location of the agricultural harvester 100 havebeen addressed. If no additional WMAs or sets of WMAs are to becontrolled at the current geographic location of the agriculturalharvester 100 remain, processing proceeds to block 646 where zonecontroller 247 determines whether additional control zones to beconsidered exist in the selected regime zone. If additional controlzones to be considered exist, processing reverts to block 624 where anext control zone is selected. If no additional control zones areremaining to be considered, processing proceeds to block 648 where adetermination as to whether additional regime zones are remaining to beconsider. Zone controller 247 determines whether additional regime zonesare remaining to be considered. If additional regimes zone are remainingto be considered, processing reverts to block 622 where a next regimezone is selected.

At block 650, zone controller 247 determines whether the operation thatagricultural harvester 100 is performing is complete. If not, the zonecontroller 247 determines whether a control zone criterion has beensatisfied to continue processing, as indicated by block 652. Forinstance, as mentioned above, control zone definition criteria mayinclude criteria defining when a control zone boundary may be crossed bythe agricultural harvester 100. For example, whether a control zoneboundary may be crossed by the agricultural harvester 100 may be definedby a selected time period, meaning that agricultural harvester 100 isprevented from crossing a zone boundary until a selected amount of timehas transpired. In that case, at block 652, zone controller 247determines whether the selected time period has elapsed. Additionally,zone controller 247 can perform processing continually. Thus, zonecontroller 247 does not wait for any particular time period beforecontinuing to determine whether an operation of the agriculturalharvester 100 is completed. At block 652, zone controller 247 determinesthat it is time to continue processing, then processing continues atblock 612 where zone controller 247 again receives an input fromgeographic position sensor 204. It will also be appreciated that zonecontroller 247 can control the WMAs and sets of WMAs simultaneouslyusing a multiple-input, multiple-output controller instead ofcontrolling the WMAs and sets of WMAs sequentially.

FIG. 12 is a block diagram showing one example of an operator interfacecontroller 231. In an illustrated example, operator interface controller231 includes operator input command processing system 654, othercontroller interaction system 656, speech processing system 658, andaction signal generator 660. Operator input command processing system654 includes speech handling system 662, touch gesture handling system664, and other items 666. Other controller interaction system 656includes controller input processing system 668 and controller outputgenerator 670. Speech processing system 658 includes trigger detector672, recognition component 674, synthesis component 676, naturallanguage understanding system 678, dialog management system 680, andother items 682. Action signal generator 660 includes visual controlsignal generator 684, audio control signal generator 686, haptic controlsignal generator 688, and other items 690. Before describing operationof the example operator interface controller 231 shown in FIG. 12 inhandling various operator interface actions, a brief description of someof the items in operator interface controller 231 and the associatedoperation thereof is first provided.

Operator input command processing system 654 detects operator inputs onoperator interface mechanisms 218 and processes those inputs forcommands. Speech handling system 662 detects speech inputs and handlesthe interactions with speech processing system 658 to process the speechinputs for commands. Touch gesture handling system 664 detects touchgestures on touch sensitive elements in operator interface mechanisms218 and processes those inputs for commands.

Other controller interaction system 656 handles interactions with othercontrollers in control system 214. Controller input processing system668 detects and processes inputs from other controllers in controlsystem 214, and controller output generator 670 generates outputs andprovides those outputs to other controllers in control system 214.Speech processing system 658 recognizes speech inputs, determines themeaning of those inputs, and provides an output indicative of themeaning of the spoken inputs. For instance, speech processing system 658may recognize a speech input from operator 260 as a settings changecommand in which operator 260 is commanding control system 214 to changea setting for a controllable subsystem 216. In such an example, speechprocessing system 658 recognizes the content of the spoken command,identifies the meaning of that command as a settings change command, andprovides the meaning of that input back to speech handling system 662.Speech handling system 662, in turn, interacts with controller outputgenerator 670 to provide the commanded output to the appropriatecontroller in control system 214 to accomplish the spoken settingschange command.

Speech processing system 658 may be invoked in a variety of differentways. For instance, in one example, speech handling system 662continuously provides an input from a microphone (being one of theoperator interface mechanisms 218) to speech processing system 658. Themicrophone detects speech from operator 260, and the speech handlingsystem 662 provides the detected speech to speech processing system 658.Trigger detector 672 detects a trigger indicating that speech processingsystem 658 is invoked. In some instances, when speech processing system658 is receiving continuous speech inputs from speech handling system662, speech recognition component 674 performs continuous speechrecognition on all speech spoken by operator 260. In some instances,speech processing system 658 is configured for invocation using a wakeupword. That is, in some instances, operation of speech processing system658 may be initiated based on recognition of a selected spoken word,referred to as the wakeup word. In such an example, where recognitioncomponent 674 recognizes the wakeup word, the recognition component 674provides an indication that the wakeup word has been recognized totrigger detector 672. Trigger detector 672 detects that speechprocessing system 658 has been invoked or triggered by the wakeup word.In another example, speech processing system 658 may be invoked by anoperator 260 actuating an actuator on a user interface mechanism, suchas by touching an actuator on a touch sensitive display screen, bypressing a button, or by providing another triggering input. In such anexample, trigger detector 672 can detect that speech processing system658 has been invoked when a triggering input via a user interfacemechanism is detected. Trigger detector 672 can detect that speechprocessing system 658 has been invoked in other ways as well.

Once speech processing system 658 is invoked, the speech input fromoperator 260 is provided to speech recognition component 674. Speechrecognition component 674 recognizes linguistic elements in the speechinput, such as words, phrases, or other linguistic units. Naturallanguage understanding system 678 identifies a meaning of the recognizedspeech. The meaning may be a natural language output, a command outputidentifying a command reflected in the recognized speech, a value outputidentifying a value in the recognized speech, or any of a wide varietyof other outputs that reflect the understanding of the recognizedspeech. For example, the natural language understanding system 678 andspeech processing system 568, more generally, may understand of themeaning of the recognized speech in the context of agriculturalharvester 100.

In some examples, speech processing system 658 can also generate outputsthat navigate operator 260 through a user experience based on the speechinput. For instance, dialog management system 680 may generate andmanage a dialog with the user in order to identify what the user wishesto do. The dialog may disambiguate a user's command; identify one ormore specific values that are needed to carry out the user's command; orobtain other information from the user or provide other information tothe user or both. Synthesis component 676 may generate speech synthesiswhich can be presented to the user through an audio operator interfacemechanism, such as a speaker. Thus, the dialog managed by dialogmanagement system 680 may be exclusively a spoken dialog or acombination of both a visual dialog and a spoken dialog.

Action signal generator 660 generates action signals to control operatorinterface mechanisms 218 based upon outputs from one or more of operatorinput command processing system 654, other controller interaction system656, and speech processing system 658. Visual control signal generator684 generates control signals to control visual items in operatorinterface mechanisms 218. The visual items may be lights, a displayscreen, warning indicators, or other visual items. Audio control signalgenerator 686 generates outputs that control audio elements of operatorinterface mechanisms 218. The audio elements include a speaker, audiblealert mechanisms, horns, or other audible elements. Haptic controlsignal generator 688 generates control signals that are output tocontrol haptic elements of operator interface mechanisms 218. The hapticelements include vibration elements that may be used to vibrate, forexample, the operator's seat, the steering wheel, pedals, or joysticksused by the operator. The haptic elements may include tactile feedbackor force feedback elements that provide tactile feedback or forcefeedback to the operator through operator interface mechanisms. Thehaptic elements may include a wide variety of other haptic elements aswell.

FIG. 13 is a flow diagram illustrating one example of the operation ofoperator interface controller 231 in generating an operator interfacedisplay on an operator interface mechanism 218, which can include atouch sensitive display screen. FIG. 13 also illustrates one example ofhow operator interface controller 231 can detect and process operatorinteractions with the touch sensitive display screen.

At block 692, operator interface controller 231 receives a map. Block694 indicates an example in which the map is a functional predictivemap, and block 696 indicates an example in which the map is another typeof map. At block 698, operator interface controller 231 receives aninput from geographic position sensor 204 identifying the geographiclocation of the agricultural harvester 100. As indicated in block 700,the input from geographic position sensor 204 can include the heading,along with the location, of agricultural harvester 100. Block 702indicates an example in which the input from geographic position sensor204 includes the speed of agricultural harvester 100, and block 704indicates an example in which the input from geographic position sensor204 includes other items.

At block 706, visual control signal generator 684 in operator interfacecontroller 231 controls the touch sensitive display screen in operatorinterface mechanisms 218 to generate a display showing all or a portionof a field represented by the received map. Block 708 indicates that thedisplayed field can include a current position marker showing a currentposition of the agricultural harvester 100 relative to the field. Block710 indicates an example in which the displayed field includes a nextwork unit marker that identifies a next work unit (or area on the field)in which agricultural harvester 100 will be operating. Block 712indicates an example in which the displayed field includes an upcomingarea display portion that displays areas that are yet to be processed byagricultural harvester 100, and block 714 indicates an example in whichthe displayed field includes previously visited display portions thatrepresent areas of the field that agricultural harvester 100 has alreadyprocessed. Block 716 indicates an example in which the displayed fielddisplays various characteristics of the field having georeferencedlocations on the map. For instance, if the received map is a weed map,the displayed field may show the different weed types existing in thefield georeferenced within the displayed field. The mappedcharacteristics can be shown in the previously visited areas (as shownin block 714), in the upcoming areas (as shown in block 712), and in thenext work unit (as shown in block 710). Block 718 indicates an examplein which the displayed field includes other items as well.

FIG. 14 is a pictorial illustration showing one example of a userinterface display 720 that can be generated on a touch sensitive displayscreen. In other implementations, the user interface display 720 may begenerated on other types of displays. The touch sensitive display screenmay be mounted in the operator compartment of agricultural harvester 100or on the mobile device or elsewhere. User interface display 720 will bedescribed prior to continuing with the description of the flow diagramshown in FIG. 13.

In the example shown in FIG. 14, user interface display 720 illustratesthat the touch sensitive display screen includes a display feature foroperating a microphone 722 and a speaker 724. Thus, the touch sensitivedisplay may be communicably coupled to the microphone 722 and thespeaker 724. Block 726 indicates that the touch sensitive display screencan include a wide variety of user interface control actuators, such asbuttons, keypads, soft keypads, links, icons, switches, etc. Theoperator 260 can actuate the user interface control actuators to performvarious functions.

In the example shown in FIG. 14, user interface display 720 includes afield display portion 728 that displays at least a portion of the fieldin which the agricultural harvester 100 is operating. The field displayportion 728 is shown with a current position marker 708 that correspondsto a current position of agricultural harvester 100 in the portion ofthe field shown in field display portion 728. In one example, theoperator may control the touch sensitive display in order to zoom intoportions of field display portion 728 or to pan or scroll the fielddisplay portion 728 to show different portions of the field. A next workunit 730 is shown as an area of the field directly in front of thecurrent position marker 708 of agricultural harvester 100. The currentposition marker 708 may also be configured to identify the direction oftravel of agricultural harvester 100, a speed of travel of agriculturalharvester 100 or both. In FIG. 14, the shape of the current positionmarker 708 provides an indication as to the orientation of theagricultural harvester 100 within the field which may be used as anindication of a direction of travel of the agricultural harvester 100.

The size of the next work unit 730 marked on field display portion 728may vary based upon a wide variety of different criteria. For instance,the size of next work unit 730 may vary based on the speed of travel ofagricultural harvester 100. Thus, when the agricultural harvester 100 istraveling faster, then the area of the next work unit 730 may be largerthan the area of next work unit 730 if agricultural harvester 100 istraveling more slowly. Field display portion 728 is also showndisplaying previously visited area 714A, 714B, 714C and 714D andupcoming areas 712A, 712B, 712C, 712D and 712E. Previously visited areas714A, 714B, 714C and 714D represent areas that are already harvestedwhile upcoming areas 712A, 712B, 712C, 712D and 712E represent areasthat still need to be harvested. The field display portion 728 is alsoshown displaying different characteristics of the field. In the exampleillustrated in FIG. 14, the map that is being displayed is a speed map.Therefore, a plurality of different speed markers are displayed on fielddisplay portion 728. There are a set of speed display markers 732 shownin the already visited areas 714A, 714B, 714C and 714D. There are also aset of speed display elements 734 shown in the upcoming areas 712A,712B, 712C, 712D, and 712E, and there are a set of speed displayelements 736 shown in the next work unit 730. FIG. 14 shows that thespeed display elements 732, 734, and 736 are made up of differentsymbols. Each of the symbols represents a speed range. In the exampleshown in FIG. 14, the @ symbol represents a low speed range of <3.5 mph;the * symbol represents a medium speed range of 3.5-5.5 mph; and the #symbol represents a high speed range of >5.5 mph. Thus, the fielddisplay portion 728 shows different speed ranges that are located atdifferent areas within the field. As described earlier, the displayelements 732 may be made up of different symbols, and, as describedbelow, the symbols may be any display feature such as different colors,shapes, patterns, intensities, text, icons, or other display features.In some instances, each location of the field may have a display markerassociated therewith. Thus, in some instances, a display marker may beprovided at each location of the field display portion 728 to identifythe nature of the characteristic being mapped for each particularlocation of the field. Consequently, the present disclosure encompassesproviding a display marker, such as the loss level display marker 732(as in the context of the present example of FIG. 11), at one or morelocations on the field display portion 728 to identify the nature,degree, etc., of the characteristic being displayed, thereby identifyingthe characteristic at the corresponding location in the field beingdisplayed.

In the example of FIG. 14, user interface display 720 also has a currentspeed indicator 721 that displays a current speed of agriculturalharvester 100 and a control display portion 738. Control display portion738 allows the operator to view information and to interact with userinterface display 720 in various ways.

The actuators and display elements in portion 738 may be displayed as,for example, individual items, fixed lists, scrollable lists, drop downmenus, or drop down lists. In the example shown in FIG. 14, displayportion 738 shows information for the three different speed ranges thatcorrespond to the three symbols mentioned above. Display portion 738also includes a set of touch sensitive actuators with which the operator260 can interact by touch. For example, the operator 260 may touch thetouch sensitive actuators with a finger to activate the respective touchsensitive actuator.

A flag column 739 shows flags that have been automatically or manuallyset. Flag actuator 740 allows operator 260 to mark a current location,and then add information indicating the speed that agriculturalharvester 100 is traveling at the current location. For instance, whenthe operator 260 actuates the flag actuator 740 by touching the flagactuator 740, touch gesture handling system 664 in operator interfacecontroller 231 identifies the current location as one where the speed isin the low speed range. When the operator 260 touches the button 742,touch gesture handling system 664 identifies the current location as alocation where the speed in the medium speed range. When the operator260 touches the button 744, touch gesture handling system 664 identifiesthe current location as a location where the speed in the high speedrange. Touch gesture handling system 664 also controls visual controlsignal generator 684 to add a symbol corresponding to the identifiedspeed on field display portion 728 at a location the user identifiesbefore or after or during actuation of buttons 740, 742 or 744.

Column 746 displays the symbols corresponding to each speed range thatis being tracked on the field display portion 728. Designator column 748shows the designator (which may be a textual designator or otherdesignator) identifying the speed range. Without limitation, the speedrange symbols in column 746 and the designators in column 748 caninclude any display feature such as different colors, shapes, patterns,intensities, text, icons, or other display features. Column 750 showsspeed range values. In the example shown in FIG. 14, the speed rangevalues are speed values in miles per hour corresponding to each speedrange. Column 752 displays action threshold values. Action thresholdvalues in column 752 may be threshold distances indicating where actions(in column 754 described below) should be taken as agriculturalharvester 100 moves through the field. In one example, when agriculturalharvester 100 is within ten feet of entering a low speed range area onthe field, then the action identified in column 754 is taken. In someexamples, operator 260 can select a threshold value, for example, inorder to change the threshold value by touching the threshold value incolumn 752. Once selected, the operator 260 may change the thresholdvalue. The threshold values in column 752 can be configured such thatthe designated action is performed when the measured distance valueexceeds the threshold value, equals the threshold value, or is less thanthe threshold value.

Similarly, operator 260 can touch the action identifiers in column 754to change the action that is to be taken. When a threshold is met,multiple actions may be taken. For instance, at the bottom of column754, an increase fan speed action and a raise header action areidentified as actions that will be taken if the measured distance valuemeets the threshold value in column 752.

The actions that can be set in column 754 may be any of a wide varietyof different types of actions. For example, the actions may include akeep out action which, when executed, inhibits agricultural harvester100 from further harvesting in an area. The actions may includemitigation activation which, when executed, performs a mitigationaction. The actions may include a setting change action for changing asetting of an internal actuator or another WMA or set of WMAs or forimplementing a settings change action that changes a setting of aheader. These are examples only, and a wide variety of other actions arecontemplated herein.

The display elements shown on user interface display 720 can be visuallycontrolled. Visually controlling the interface display 720 may beperformed to capture the attention of operator 260. For instance, thedisplay elements can be controlled to modify the intensity, color, orpattern with which the display elements are displayed. Additionally, thedisplay elements may be controlled to flash. The described alterationsto the visual appearance of the display elements are provided asexamples. Consequently, other aspects of the visual appearance of thedisplay elements may be altered. Therefore, the display elements can bemodified under various circumstances in a desired manner in order, forexample, to capture the attention of operator 260.

Various functions that can be accomplished by the operator 260 usinguser interface display 720 can also be accomplished automatically, suchas by other controllers in control system 214. For instance, it may bethat agricultural harvester 100 consistently travels at a speed of5.0-6.0 mph. In that case, the operator interface controller 231 can adda new speed range, such as a “high-mid range” of 5.0-6.0 mph and canautomatically add a flag at the current location of agriculturalharvester 100 (which corresponds to the location of the new speed range)and generate a display in the flag column, a corresponding symbol in thesymbol column, and a designator in the designator column 748. Theoperator interface controller 231 can also generate a speed range valuein column 750 and a threshold value in column 752 upon identification ofa different speed range. Operator interface controller 231, or anothercontroller, can also automatically identify an action that is added tocolumn 754.

Returning now to the flow diagram of FIG. 13, the description of theoperation of operator interface controller 231 continues. At block 760,operator interface controller 231 detects an input setting a flag andcontrols the touch sensitive user interface display 720 to display theflag on field display portion 728. The detected input may be an operatorinput, as indicated at 762, or an input from another controller, asindicated at 764. At block 766, operator interface controller 231detects an in-situ sensor input indicative of a measured characteristicof the field from one of the in-situ sensors 208. At block 768, visualcontrol signal generator 684 generates control signals to control userinterface display 720 to display actuators for modifying user interfacedisplay 720 and for modifying machine control. For instance, block 770represents that one or more of the actuators for setting or modifyingthe values in columns 739, 746, and 748 can be displayed. Thus, the usercan set flags and modify characteristics of those flags. For example, auser can modify the speed ranges corresponding to the flags. Block 772represents that action threshold values in column 752 are displayed.Block 776 represents that the actions in column 754 are displayed, andblock 778 represents that the measured in-situ data in column 750 isdisplayed. Block 780 indicates that a wide variety of other informationand actuators can be displayed on user interface display 720 as well.

At block 782, operator input command processing system 654 detects andprocesses operator inputs corresponding to interactions with the userinterface display 720 performed by the operator 260. Where the userinterface mechanism on which user interface display 720 is displayed isa touch sensitive display screen, interaction inputs with the touchsensitive display screen by the operator 260 can be touch gestures 784.In some instances, the operator interaction inputs can be inputs using apoint and click device 786 or other operator interaction inputs 788.

At block 790, operator interface controller 231 receives signalsindicative of an alert condition. For instance, block 792 indicates thatsignals may be received by controller input processing system 668indicating that detected values in column 750 satisfy thresholdconditions present in column 752. As explained earlier, the thresholdconditions may include values being below a threshold, at a threshold,or above a threshold. Block 794 shows that action signal generator 660can, in response to receiving an alert condition, alert the operator 260by using visual control signal generator 684 to generate visual alerts,by using audio control signal generator 686 to generate audio alerts, byusing haptic control signal generator 688 to generate haptic alerts, orby using any combination of these. Similarly, as indicated by block 796,controller output generator 670 can generate outputs to othercontrollers in control system 214 so that those controllers perform thecorresponding action identified in column 754. Block 798 shows thatoperator interface controller 231 can detect and process alertconditions in other ways as well.

Block 900 shows that speech handling system 662 may detect and processinputs invoking speech processing system 658. Block 902 shows thatperforming speech processing may include the use of dialog managementsystem 680 to conduct a dialog with the operator 260. Block 904 showsthat the speech processing may include providing signals to controlleroutput generator 670 so that control operations are automaticallyperformed based upon the speech inputs.

Table 1, below, shows an example of a dialog between operator interfacecontroller 231 and operator 260. In Table 1, operator 260 uses a triggerword or a wakeup word that is detected by trigger detector 672 to invokespeech processing system 658. In the example shown in Table 1, thewakeup word is “Johnny”.

TABLE 1 Operator: “Johnny, tell me about the current speed” OperatorInterface Controller: “Current speed is at 3.7 mph in the medium rangewith threshold of 20 feet. A high speed range is approaching and is 120feet away.” Operator: “Johnny, what should I do because of the speedchange approaching?” Operator Interface Controller: “Increase fan speedby 10% and raise the header by 10%.”

Table 2 shows an example in which speech synthesis component 676provides an output to audio control signal generator 686 to provideaudible updates on an intermittent or periodic basis. The intervalbetween updates may be time-based, such as every five minutes, orcoverage or distance-based, such as every five acres, orexception-based, such as when a measured value is greater than athreshold value.

TABLE 2 Operator Interface Controller: “Over last 10 minutes, the travelspeed has been 5.0 mph.” Operator Interface Controller: “Next 1 acrecomprises a high speed range.” Operator Interface Controller: “Warning:Current speed is above the desired speed range. Slow machine speed to5.0 mph.” Operator Interface Controller: “Caution: biomass is too high.Machine speed decreased to 4.0 mph.”

The example shown in Table 3 illustrates that some actuators or userinput mechanisms on the touch sensitive display 720 can be supplementedwith speech dialog. The example in Table 3 illustrates that actionsignal generator 660 can generate action signals to automatically mark ahigh speed range in the field being harvested.

TABLE 3 Human: “Johnny, mark high speed range.” Operator InterfaceController: “High speed range marked.”

The example shown in Table 4 illustrates that action signal generator660 can conduct a dialog with operator 260 to begin and end marking of aspeed range.

TABLE 4 Human: “Johnny, start marking high speed range.” OperatorInterface Controller: “Marking high speed range.” Human: “Johnny, stopmarking high speed range.” Operator Interface Controller: “High speedrange marking stopped.”

The example shown in Table 5 illustrates that action signal generator160 can generate signals to mark a speed range in a different way thanthose shown in Tables 3 and 4.

TABLE 5 Human: “Johnny, mark next 100 feet as high speed range.”Operator Interface Controller: “Next 100 feet marked as high speedrange.”

Returning again to FIG. 13, block 906 illustrates that operatorinterface controller 231 can detect and process conditions foroutputting a message or other information in other ways as well. Forinstance, other controller interaction system 656 can detect inputs fromother controllers indicating that alerts or output messages should bepresented to operator 260. Block 908 shows that the outputs can be audiomessages. Block 910 shows that the outputs can be visual messages, andblock 912 shows that the outputs can be haptic messages. Until operatorinterface controller 231 determines that the current harvestingoperation is completed, as indicated by block 914, processing reverts toblock 698 where the geographic location of harvester 100 is updated andprocessing proceeds as described above to update user interface display720.

Once the operation is complete, then any desired values that aredisplayed, or have been displayed on user interface display 720, can besaved. Those values can also be used in machine learning to improvedifferent portions of predictive model generator 210, predictive mapgenerator 212, control zone generator 213, control algorithms, or otheritems. Saving the desired values is indicated by block 916. The valuescan be saved locally on agricultural harvester 100, or the values can besaved at a remote server location or sent to another remote system.

It can thus be seen that an information map is obtained by anagricultural harvester and shows machine speed values at differentgeographic locations of a field being harvested. An in-situ sensor onthe harvester senses a characteristic as the agricultural harvestermoves through the field. A predictive map generator generates apredictive map that predicts control values for different locations inthe field based on the values of the machine speed in the informationmap and the characteristic sensed by the in-situ sensor. A controlsystem controls controllable subsystem based on the control values inthe predictive map.

A control value is a value upon which an action can be based. A controlvalue, as described herein, can include any value (or characteristicsindicated by or derived from the value) that may be used in the controlof agricultural harvester 100. A control value can be any valueindicative of an agricultural characteristic. A control value can be apredicted value, a measured value, or a detected value. A control valuemay include any of the values provided by a map, such as any of the mapsdescribed herein, for instance, a control value can be a value providedby an information map, a value provided by prior information map, or avalue provided predictive map, such as a functional predictive map. Acontrol value can also include any of the characteristics indicated byor derived from the values detected by any of the sensors describedherein. In other examples, a control value can be provided by anoperator of the agricultural machine, such as a command input by anoperator of the agricultural machine.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, including but not limited toartificial intelligence components, such as neural networks, some ofwhich are described below, that perform the functions associated withthose systems, components, logic, or interactions. In addition, any orall of the systems, components, logic and interactions may beimplemented by software that is loaded into a memory and is subsequentlyexecuted by a processor or server or other computing component, asdescribed below. Any or all of the systems, components, logic andinteractions may also be implemented by different combinations ofhardware, software, firmware, etc., some examples of which are describedbelow. These are some examples of different structures that may be usedto implement any or all of the systems, components, logic andinteractions described above. Other structures may be used as well.

FIG. 15 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2. The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 15, some items are similar to those shownin FIG. 2 and those items are similarly numbered. FIG. 15 specificallyshows that predictive model generator 210 or predictive map generator212, or both, may be located at a server location 502 that is remotefrom the agricultural harvester 600. Therefore, in the example shown inFIG. 15, agricultural harvester 600 accesses systems through remoteserver location 502.

FIG. 15 also depicts another example of a remote server architecture.FIG. 15 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2, or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 16 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.17-18 are examples of handheld or mobile devices.

FIG. 16 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2, that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 17 shows one example in which device 16 is a tablet computer 600.In FIG. 17, computer 601 is shown with user interface display screen602. Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 601 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 601 may also illustratively receive voice inputs as well.

FIG. 18 is similar to FIG. 17 except that the device is a smart phone71. Smart phone 71 has a touch sensitive display 73 that displays iconsor tiles or other user input mechanisms 75. Mechanisms 75 can be used bya user to run applications, make calls, perform data transferoperations, etc. In general, smart phone 71 is built on a mobileoperating system and offers more advanced computing capability andconnectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 19 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 19, an example system forimplementing some embodiments includes a computing device in the form ofa computer 810 programmed to operate as discussed above. Components ofcomputer 810 may include, but are not limited to, a processing unit 820(which can comprise processors or servers from previous FIGS.), a systemmemory 830, and a system bus 821 that couples various system componentsincluding the system memory to the processing unit 820. The system bus821 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. Memory and programs described with respectto FIG. 2 can be deployed in corresponding portions of FIG. 19.

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 19 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 19 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 19, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 19, for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 19 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

Example 1 is an agricultural work machine comprising

a communication system that receives an information map that includesvalues of a machine speed corresponding to different geographiclocations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristiccorresponding to the geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive control values to thedifferent geographic locations in the field based on the values of themachine speed in the information map and based on the value of theagricultural characteristic;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 2 is the agricultural work machine of any or all previousexamples, wherein the information map comprises:

a predictive machine speed map that maps, as the control values, machinespeed values indicative of predicted speed of the agricultural harvesterat the different locations in the field.

Example 3 is the agricultural work machine of any or all previousexamples, and further comprising:

a speed-to-in-situ sensor data model generator that generates apredictive sensor data model that models a relationship between thepredictive machine speed values and the agricultural characteristicbased on the predictive machine speed values in the predictive machinespeed map at the geographic location and the value of the agriculturalcharacteristic corresponding to the geographic location.

Example 4 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive sensor data map generator that generates a functionalpredictive sensor data map that maps predictive values of theagricultural characteristic to the different geographic locations in thefield.

Example 5 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a controller that generates a control signal, based on the geographiclocation and the functional predictive sensor data map, and controls thecontrollable subsystem based on the control signal.

Example 6 is the agricultural work machine of any or all previousexamples, wherein the control system further comprises:

an operator interface controller that generates a user interface maprepresentation of the functional predictive agricultural map, the userinterface map representation comprising a field portion.

Example 7 is the agricultural work machine of any or all previousexamples, wherein the user interface map representation furthercomprises:

a machine speed symbol indicating a value of the machine speed at one ormore geographic locations on the field portion.

Example 8 is the agricultural work machine of any or all previousexamples, wherein the operator interface controller generates the userinterface map representation to include an interactive display portionthat displays a detected characteristic display indicative of thedetected agricultural characteristic, an interactive threshold displayportion indicative of an action threshold, and an interactive actionindicator indicative of a control action to be taken when the detectedagricultural characteristic satisfies the action threshold, the controlsystem generating the control signal to control the controllablesubsystem based on the control action.

Example 0 is a computer implemented method of controlling anagricultural work machine comprising

obtaining an information map that includes values of machine speedcorresponding to different geographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of an agriculturalcharacteristic corresponding to the geographic location;

generating a functional predictive agricultural map of the field thatmaps predictive control values to the different geographic locations inthe field based on the values of the machine speed in the informationmap and based on the value of the agricultural characteristic; and

controlling a controllable subsystem based on the geographic position ofthe agricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 10 is the computer implemented method of any or all previousexamples, wherein obtaining an information map comprises:

obtaining a predictive machine speed map that maps machine speed valuesindicative of predicted speed of the agricultural harvester at thedifferent locations in the field.

Example 11 is the computer implemented method of any or all previousexamples, and further comprising:

generating, with a speed-to-in-situ model generator, a predictive sensordata model that models a relationship between the predictive machinespeed values and the agricultural characteristic based on the predictivemachine speed value in the predictive machine speed map at thegeographic location and the value of the agricultural characteristiccorresponding to the geographic location.

Example 12 is the computer implemented method of any or all previousexamples, and further comprising:

generating, with a predictive sensor data map generator, a functionalpredictive sensor data map, as the functional predictive agriculturalmap, that maps, as the predictive control values, predictive values ofthe agricultural characteristic to the different geographic locations inthe field.

Example 13 is the computer implemented method of any or all previousexamples, and further comprising:

generating a control signal, based on the geographic location and thefunctional predictive sensor data map, and controls the controllablesubsystem based on the control signal.

Example 14 is the computer implemented method of any or all previousexamples, and further comprising:

generating a user interface map representation of the functionalpredictive agricultural map, the user interface map representationcomprising a field portion.

Example 15 is the computer implemented method of any or all previousexamples, wherein generating the user interface map representation ofthe functional predictive agricultural map further comprises:

generating, as part of the user interface map representation, a machinespeed symbol indicating a value of the machine speed at one or moregeographic locations on the field portion.

Example 16 is the computer implemented method of any or all previousexamples, generating the user interface map representation of thefunctional predictive agricultural map further comprises:

generating, as part of the user interface map representation, aninteractive display portion that displays a detected characteristicdisplay indicative of the detected agricultural characteristic, aninteractive threshold display portion indicative of an action threshold,and an interactive action indicator indicative of a control action to betaken when the detected agricultural characteristic satisfies the actionthreshold, the control system generating the control signal to controlthe controllable subsystem based on the control action.

Example 18 is an agricultural work machine comprising:

a communication system that receives an information map that includesvalues of a machine speed corresponding to different geographiclocations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristiccorresponding to the geographic location;

a predictive model generator that generates a predictive agriculturalmodel that models a relationship between the machine speed and theagricultural characteristic based on a value of the machine speed in theinformation map at the geographic location and a value of theagricultural characteristic sensed by the in-situ sensor correspondingto the geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive control values to thedifferent geographic locations in the field based on the values of themachine speed in the information map and based on the predictiveagricultural model;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 18 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor generates a sensor signalindicative of the agricultural characteristic and further comprises:

a processing system that receives the sensor signal and is configured toidentify the value of the agricultural characteristic corresponding tothe geographic location, based on the sensor signal.

Example 19 is the agricultural work machine of any or all previousexamples, wherein the control system further comprises:

an operator interface controller that generates a user interface maprepresentation of the functional predictive agricultural map, the userinterface map representation comprising a field portion and a machinespeed symbol indicating a value of the machine speed at one or moregeographic locations on the field portion.

Example 20 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive sensor data map generator that generates, as the functionalpredictive agricultural map, a functional predictive sensor data mapthat maps, as the predictive control values, predictive values of theagricultural characteristic to the different geographic locations in thefield.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

What is claimed is:
 1. An agricultural system comprising: acommunication system that receives an information map that includesvalues of a machine speed corresponding to different geographiclocations in a field; a geographic position sensor that detects ageographic location of an agricultural work machine; an in-situ sensorthat detects a value of an agricultural characteristic corresponding tothe geographic location; a predictive map generator that generates afunctional predictive agricultural map of the field that maps predictivecontrol values to the different geographic locations in the field basedon the values of the machine speed in the information map and based onthe value of the agricultural characteristic; and a control system thatgenerates a control signal to control a controllable subsystem of theagricultural work machine based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.
 2. The agricultural system ofclaim 1, wherein the information map comprises: a predictive machinespeed map that maps, as the control values, machine speed valuesindicative of predicted speed of the agricultural work machine at thedifferent locations in the field.
 3. The agricultural system of claim 2,and further comprising: a speed-to-in-situ sensor data model generatorthat generates a predictive sensor data model that models a relationshipbetween the predictive machine speed values and the agriculturalcharacteristic based on the predictive machine speed values in thepredictive machine speed map at the geographic location and the value ofthe agricultural characteristic corresponding to the geographiclocation.
 4. The agricultural system of claim 3, wherein the predictivemap generator comprises: a predictive sensor data map generator thatgenerates a functional predictive sensor data map that maps predictivevalues of the agricultural characteristic to the different geographiclocations in the field.
 5. The agricultural system of claim 4, whereinthe control system comprises: a controller that generates a controlsignal, based on the geographic location and the functional predictivesensor data map, and controls the controllable subsystem based on thecontrol signal.
 6. The agricultural system of claim 1, wherein thecontrol system further comprises: an operator interface controller thatgenerates a user interface map representation of the functionalpredictive agricultural map, the user interface map representationcomprising a field portion.
 7. The agricultural system of claim 6,wherein the user interface map representation further comprises: amachine speed symbol indicating a value of the machine speed at one ormore geographic locations on the field portion.
 8. The agriculturalsystem of claim 7, wherein the operator interface controller generatesthe user interface map representation to include an interactive displayportion that displays a detected characteristic display indicative ofthe detected agricultural characteristic, an interactive thresholddisplay portion indicative of an action threshold, and an interactiveaction indicator indicative of a control action to be taken when thedetected agricultural characteristic satisfies the action threshold, thecontrol system generating the control signal to control the controllablesubsystem based on the control action.
 9. A computer implemented methodof controlling an agricultural work machine comprising: obtaining aninformation map that includes values of machine speed corresponding todifferent geographic locations in a field; detecting a geographiclocation of the agricultural work machine; detecting, with an in-situsensor, a value of an agricultural characteristic corresponding to thegeographic location; generating a functional predictive agricultural mapof the field that maps predictive control values to the differentgeographic locations in the field based on the values of the machinespeed in the information map and based on the value of the agriculturalcharacteristic; and controlling a controllable subsystem based on thegeographic position of the agricultural work machine and based on thecontrol values in the functional predictive agricultural map.
 10. Thecomputer implemented method of claim 9, wherein obtaining an informationmap comprises: obtaining a predictive machine speed map that mapsmachine speed values indicative of predicted speed of the agriculturalwork machine at the different locations in the field.
 11. The computerimplemented method of claim 10, and further comprising: generating, witha speed-to-in-situ model generator, a predictive sensor data model thatmodels a relationship between the predictive machine speed values andthe agricultural characteristic based on the predictive machine speedvalue in the predictive machine speed map at the geographic location andthe value of the agricultural characteristic corresponding to thegeographic location.
 12. The computer implemented method of claim 11,and further comprising: generating, with a predictive sensor data mapgenerator, a functional predictive sensor data map, as the functionalpredictive agricultural map, that maps, as the predictive controlvalues, predictive values of the agricultural characteristic to thedifferent geographic locations in the field.
 13. The computerimplemented method of claim 12, and further comprising: generating acontrol signal, based on the geographic location and the functionalpredictive sensor data map, and controlling the controllable subsystembased on the control signal.
 14. The computer implemented method ofclaim 9, and further comprising: generating a user interface maprepresentation of the functional predictive agricultural map, the userinterface map representation comprising a field portion.
 15. Thecomputer implemented method of claim 14, wherein generating the userinterface map representation of the functional predictive agriculturalmap further comprises: generating, as part of the user interface maprepresentation, a machine speed symbol indicating a value of the machinespeed at one or more geographic locations on the field portion.
 16. Thecomputer implemented method of claim 15, generating the user interfacemap representation of the functional predictive agricultural map furthercomprises: generating, as part of the user interface map representation,an interactive display portion that displays a detected characteristicdisplay indicative of the detected agricultural characteristic, aninteractive threshold display portion indicative of an action threshold,and an interactive action indicator indicative of a control action to betaken when the detected agricultural characteristic satisfies the actionthreshold, the control system generating the control signal to controlthe controllable subsystem based on the control action.
 17. Anagricultural system comprising: a communication system that receives aninformation map that includes values of a machine speed corresponding todifferent geographic locations in a field; a geographic position sensorthat detects a geographic location of an agricultural work machine; anin-situ sensor that detects a value of an agricultural characteristiccorresponding to the geographic location; a predictive model generatorthat generates a predictive agricultural model that models arelationship between the machine speed and the agriculturalcharacteristic based on a value of the machine speed in the informationmap at the geographic location and a value of the agriculturalcharacteristic sensed by the in-situ sensor corresponding to thegeographic location; a predictive map generator that generates afunctional predictive agricultural map of the field that maps predictivecontrol values to the different geographic locations in the field basedon the values of the machine speed in the information map and based onthe predictive agricultural model; a control system that generates acontrol signal to control a controllable subsystem of the agriculturalwork machine based on the geographic position of the agricultural workmachine and based on the control values in the functional predictiveagricultural map.
 18. The agricultural system of claim 17, wherein thein-situ sensor generates a sensor signal indicative of the agriculturalcharacteristic and further comprises: a processing system that receivesthe sensor signal and is configured to identify the value of theagricultural characteristic corresponding to the geographic location,based on the sensor signal.
 19. The agricultural system of claim 17,wherein the control system further comprises: an operator interfacecontroller that generates a user interface map representation of thefunctional predictive agricultural map, the user interface maprepresentation comprising a field portion and a machine speed symbolindicating a value of the machine speed at one or more geographiclocations on the field portion.
 20. The agricultural system of claim 17,wherein the predictive map generator comprises: a predictive sensor datamap generator that generates, as the functional predictive agriculturalmap, a functional predictive sensor data np that maps, as the predictivecontrol values, predictive values of the agricultural characteristic tothe different geographic locations in the field.